{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Perceptron ",
   "id": "e5e0656816d79ad6"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 数据准备与预处理",
   "id": "ea9227f1ca2b7699"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-12T13:08:57.446864Z",
     "start_time": "2025-06-12T13:08:57.233865Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:47.865340Z",
     "start_time": "2025-06-12T13:08:57.451864Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 加载MNIST数据集\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)"
   ],
   "id": "cdc36b8612a00cee",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\sklearn\\datasets\\_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:49.992359Z",
     "start_time": "2025-06-12T13:09:48.903075Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "id": "c3f76b3850738eb7",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:50.984401Z",
     "start_time": "2025-06-12T13:09:50.057358Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ],
   "id": "30c4c961aa14dd48",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 感知机模型训练与评估",
   "id": "e813c6e1b5ab2401"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:09:51.219687Z",
     "start_time": "2025-06-12T13:09:51.126687Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ],
   "id": "7480d230bfff3df1",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:10:01.189473Z",
     "start_time": "2025-06-12T13:09:51.233687Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 感知机模型训练\n",
    "print(\"\\n训练感知机模型中...\")\n",
    "perceptron = Perceptron(random_state=42, max_iter=100, tol=1e-3)\n",
    "perceptron.fit(X_train, y_train)"
   ],
   "id": "c8071bc50bc3d5c5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练感知机模型中...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Perceptron(max_iter=100, random_state=42)"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Perceptron(max_iter=100, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Perceptron</label><div class=\"sk-toggleable__content\"><pre>Perceptron(max_iter=100, random_state=42)</pre></div></div></div></div></div>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:56.121706Z",
     "start_time": "2025-06-12T13:10:01.734974Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import timeit\n",
    "\n",
    "# 重复10次取平均值（减少随机误差）\n",
    "num_runs = 10\n",
    "total_time = timeit.timeit(\n",
    "    'model.fit(X_train, y_train)', \n",
    "    setup='''\n",
    "from sklearn.linear_model import Perceptron\n",
    "model = Perceptron(random_state=42)# 每次测试前重置模型\n",
    "    ''',\n",
    "    globals={'X_train': X_train, 'y_train': y_train},\n",
    "    number=num_runs\n",
    ")\n",
    "print(f\"平均训练时间({num_runs}次): {total_time/num_runs:.4f} 秒\")"
   ],
   "id": "a537090e75367918",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均训练时间(10次): 17.4362 秒\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:56.231550Z",
     "start_time": "2025-06-12T13:12:56.185456Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 模型预测\n",
    "y_pred = perceptron.predict(X_test)"
   ],
   "id": "2d4fabe83afdf63",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:56.278553Z",
     "start_time": "2025-06-12T13:12:56.236453Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 模型评估\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)"
   ],
   "id": "856780f8582cb1a7",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:56.340466Z",
     "start_time": "2025-06-12T13:12:56.326483Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"\\n感知机模型评估结果：\")\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "print(f\"F1-score: {f1:.4f}\")\n",
    "print(\"\\nConfusion Matrix:\\n\", conf_matrix)\n",
    "print(\"\\nClassification Report:\\n\", class_report)"
   ],
   "id": "91b5fa2f90d49871",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "感知机模型评估结果：\n",
      "Accuracy: 0.8811\n",
      "Precision: 0.8810\n",
      "Recall: 0.8811\n",
      "F1-score: 0.8808\n",
      "\n",
      "Confusion Matrix:\n",
      " [[1276    2   21    7    1    7   17    4    8    0]\n",
      " [   1 1503    6   10    4   13    1   29   31    2]\n",
      " [   7   15 1231   27   15   17   14   14   33    7]\n",
      " [   9   16   34 1221    4   33   12   35   42   27]\n",
      " [   3    3   14    6 1160    6   12   10   24   57]\n",
      " [  10   13   15   59   21 1034   29    6   75   11]\n",
      " [   4   20   52    3   10   11 1277    2   14    3]\n",
      " [   8    4   15    6   13   16    0 1394    6   41]\n",
      " [  11   42   21   57    8   70   12   13 1089   34]\n",
      " [   8    3    4   24   72   26    0  101   32 1150]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95      1343\n",
      "           1       0.93      0.94      0.93      1600\n",
      "           2       0.87      0.89      0.88      1380\n",
      "           3       0.86      0.85      0.86      1433\n",
      "           4       0.89      0.90      0.89      1295\n",
      "           5       0.84      0.81      0.83      1273\n",
      "           6       0.93      0.91      0.92      1396\n",
      "           7       0.87      0.93      0.90      1503\n",
      "           8       0.80      0.80      0.80      1357\n",
      "           9       0.86      0.81      0.84      1420\n",
      "\n",
      "    accuracy                           0.88     14000\n",
      "   macro avg       0.88      0.88      0.88     14000\n",
      "weighted avg       0.88      0.88      0.88     14000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:56.684059Z",
     "start_time": "2025-06-12T13:12:56.388453Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 混淆矩阵可视化\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=range(10), yticklabels=range(10))\n",
    "plt.title(\"Perceptron - Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "id": "f859154fb09bc396",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ],
      "image/png": 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wbds2zJo1C71798aDBw+wadMmmQnGDRo0wNy5c7F06VI0bdoUr169gr+/PywsLGBlZZXnOYcOHYojR45gyJAhcHV1hYGBAQ4dOoSrV69iyZIl3/zF/VPat2+PrVu3wsDA4JMXrrO1tcXq1auxbt061KlTB48fP0ZAQADEYrHMHA59fX3cvXsXwcHBnzw718devHiBefPmoUmTJujWrRsAoHXr1vD09ETjxo2VelFAIiKhsFNBRJ/1448/QldXFxs2bEBQUBBKliyJunXrwsfH54sTUadOnQpDQ0Ps3r0bGzZsQMWKFTFnzhz07dsXQM7k2cDAQPj7+2P8+PEoUaIEatasiU2bNuWZwDtv3jwEBAQgJiYGNjY22Lhxo0znYfTo0Vi9ejVGjBiBY8eOfTLT4sWLYW5ujv3792P9+vUwMTHB4MGDMXbsWIV/2f2cJk2aYO/evdi4cSMCAgKQlJQEAwMD1KpVC0FBQbCzs5Ou27NnT+jq6iIgIAAuLi4oVaoUnJ2d4ebmlmd+iLwaN26M6dOnY9u2bTh+/Dhq1qwJf39/6T4CgL59+yIzMxO7d+/Gzp07oa2tjYYNG2Lq1Kn5DjUyNjbGrl274Ovri0WLFiEzMxNWVlZYvXo1WrVqVaC8+flwFqwOHTp8ch+OGjUKL1++xNatW7Fq1SqUL18e3bp1g0gkQkBAAF69egV9fX0MGzYMS5YswfDhw7Fp06avev358+cjLS1NZt6Rh4cHOnbsiFmzZkmvFUJEpMpEkoLO4CMiKkQHDhyAu7v7Jyc9ExERkfA4p4KIiIiIiAqEnQoiIiIiIioQDn8iIiIiIqICYaWCiIiIiIgKhJ0KIiIiIiIqEHYqiIiIiIioQNipICIiIiKiAlHJi9/p990qdARBJGwfLHQEQWRnF89zDYhEIqEjCKKYbjaK6yk1JCieG65WTA/04nqcF9PdDe3v+Fuojr2r0l4r7aa/0l6rMLFSQUREREREBfId9xGJiIiIiAQg4u/u8uI7RkREREREBcJKBRERERFRbsV1oksBsFJBREREREQFwkoFEREREVFunFMhN75jRERERERUIKxUEBERERHlxjkVcmOlgoiIiIiICoSVCiIiIiKi3DinQm58x4iIiIiIqEBYqSAiIiIiyo1zKuTGSgURERERERUIKxVERERERLlxToXc+I4REREREVGBsFNBREREREQFwuFPRERERES5caK23FipICIiIiKiAmGlgoiIiIgoN07UlhvfsS/Q1FDDVe8uaGJjKm2rX80Ify1oj+eb+yHUrxsGt6gmXXZ7ZU+82j04z216T1vpOjN72yFi7Y94vOEnLB/RAFoliu5uiI+Px+SJ4+Hc0BGtWzjDe6knMjIyhI6lcAnx8ZjiNh7NGjuhbaum8PHKu51PnjxGg3p2AiUsfIcPHUCdWjXy3OxrWwkdTWnEYjF6duuMkOBrQkcpdMVxf4vFYvTu3gXX/92/HrNmwL6WVZ7byGE/C5y08LmOGYk5M2cIHUMp4mJjMW7sKDR2qosObVti+7bNQkdSioyMDMydMxNNGtRDq2ZNsGXzRqEjURHHSsVnaJVQQ+A4Z9hUKiNtMymtjX0zWiHw5AOMXn0JdaoYYvWYRohPScPxm8/QfObvUFf7bxxe9wbmmN3HHjvPRwIAJnWthV/a1sCQ5efxNj0TgeOcMaOXHebvvqn07SsoiUSCKZPGQ19fH5u27cCr1FTMnT0T6upqcJsyXeh4CiORSDDFbTz09Utj45btSE1NxTyPWVBXV8ekydMAAHFxsZjgMlolO1QftGvfEY2bOEvvv898jxHDf0bTZs2FC6VEGRkZmDFtMiIjHgodRSmK2/7OyMjAzGlTZPbv1BmzMH7SZOn958+eYcTQweg3YJAQEZXmj2O/48L5c+jarYfQUZRi2pSJKF/eDDv3HEBUZATcp0+BWfkKaNm6jdDRCpWfjxfu3rmD9Ru34Pnz55gzczrMypuhTbv2Qkf7PnBOhdzYqfiEGhVKI3Ccc55jqnP9ykhITcOCfzsBkXGv4VyzHH5sXAXHbz5D8uv/vlTq65TA9J62mLX9OmKS3kJNJIJrJ2vM3n4d5/+OAwAs2RuG/s0slbZdivQoOgrhYbdw+twlGBoZAQDGuo6Hr89SlepUPIqOxu3wMJw8c1G6nWNcxmGZrxcmTZ6GM6dOYuECDxgZGQuctHBpa2tDW1tbej9wfQAgkWDCpCkCplKOyIgIuE+bDIlEInQUpSlO+zsyMgIzp03Js3/19PSgp6cnve8xcwbatG2PFq1aKzui0qSmpGCZrxdq1qotdBSleJWaivCwW/CYtxDm5hYwN7dA48bOuHbtikp3Kt69e4eD+/di1dr1sLapCWubmoiMeIjdu3awU0HfrOiOuylkTWxMceFuHFrP+UOm/WTYM4xdcznP+volNfO0je9SE3Epadh+NgIAYF2pNAz1tHE0JEa6zp5L0ei+5KSC0yuHoZExVgdskH7R/uDN6zcCJSocRkZGWLV2/Se388KFcxjrMh7TZswUIp4gUlNTsGnjeoyfNBmamnmPfVUTej0Y9R2dsHVnkNBRBKHq+zs0JAT1HZ2wZcfuT65z7eoV3Ai9DtcJk5SYTPl8fZaic5dusLSs9uWVVYCWtja0dXRw+NABZGZm4lF0FG7dvAErK2uhoxWqB//cx/v371Gnjr20zb6uA26HhyE7O1vAZN8RkZrybiriu6hUvHz5EmKxGDo6OtDX1xc6DgAg8K8H+bY/SXyLJ4lvpfeN9LXRq5EFft0XJrOejqY6RrazwsQNV/Hhxy8LEz28fJMBpxrG8PipLgz1tXDk2mN47LwB8fui90esr68vMzwiOzsbu3duh1ODBgKmUjw9fX00aiy7nUG7dsDRKWc7PeYtBABcD1H9cfYf7Nm9CybGJmjTtnj8otWnb3+hIwhK1fd3n779vrjOpg3r0aVbD5QrX14JiYRx7eoV3Lh+HfsO/Q+LF8wTOo5SaGlpwX2WB35dvBA7t29FVlYWunbviR69fhQ6WqFKSkyEgUEZlMj1I4GhoREyMjKQkpKCsmXLCpiOiirBOhUnTpzA9u3bER4eLjMOXVtbG7Vq1cLPP/+M1q2/7xKzdgl1bHdrhoSUNGw8KdsJ6dnQAm/TM3H42mNpWyltDehoaWBev7pw33od6moi/PZLA6irqWHq5mBlx1e4Zb7euHfvLnYE7RM6SqH6zc8b9+/dxfZde4WOIgiJRIKDB/ZiyNBfhI5CSsD9DTyNiUFI8FVMdVfdamRGRgYWzZ8L99keMsPeioPoqEg0a94Cg34eioiIh1i6ZCGcGjREp85dhY5WaNLS0/JUHT/czxSLhYj0/eGcCrkJ0qnYtGkT/P398csvv8DV1RWGhobQ1NSEWCxGUlISrl+/jhkzZmDChAkYNOj7nBCnq6WBXVNboFo5fbSb9yfSxFkyy7s7mePAlUfIyv5vjO77LAlKamlg2uYQXLoXDwCYue06No53xrQtwSjKw7WX+Xpjx7Yt8PJZhurVfxA6TqFZ7ueDndu34ldvP1RT4e38nL/v3EZCfDzad+gkdBRSAu5v4NRfJ1DDykqlhwStXe0Pm5q1ZKrPxcG1q1dwcP8+HD91Dtra2qhZqzYS4uOxIWCNSncqtLS0IP6o8/DhfnHrVJLiCNKp2LhxI5YuXZpvJcLS0hJOTk6oUaMGFi5c+F12KvR0SmD/jFaoaqqHzotOIDLutcxyTQ01NLExhd+ROzLtcSlpAIAHz1OlbQ+fv4KOpgaM9LWRmJpe+OELgefihdgbtAuLf/VG67bthI5TaH5dshD79uzGIk8vtG6jutv5JZcvXUBdh3rQL11a6CikBNzfOe9B85bfd+W8oP7843ckJyWhQb2cMfaZmTlfMP86cRxXrxe9sxN+rbt376CyubnMF2kraxsErl8rYKrCZ2JiipSUl3j//j00NHK+CiYlJUJbWxt638kwdMGp0FwHZRGkU5Geno6KFSt+dh1TU1O8fv36s+sIQSQCtrs1h4VJKXRYcBwPn7/Ks07NymVQQkMNoRFJMu3hj14gIzMLtc3L4HR4LICcs0y9eifGi9dF81Ska1f7Y9+e3Vjq7afSZ4wIWOOP/XuD4Onlq7Ljyr/W7fBw1LGvK3QMUpLivr8lEgn+vnMbw0eOFjpKoQrcvA3vM99L7//m5wMAmOimemf7ys3E2AQxTx4jM1OMEiVyhv88io6CWYXPf0cp6mpYWUNDQwPhYbdQ16EeAODmjVDUrFUbamr8Mk3fRpAjp02bNpgxYwauX7+O9+/fyyzLzs7GjRs3MHPmTLRr9/39Gjy4RXU0rWmKceuuIPWtGCaltWFSWhtldP8bm2hdyQCP4t/kmXz9Oi0TW04/hPcQR9SvZgTH6kZY0L8utp6JkBkmVVRERUZi3drVGDp8BOzrOiApMVF6UyVRUZFYH7AGQ4b9u51JidJbcRQR8RBVq6ruMBCSVdz3d+zzZ3j79i2qWhbNU39/LTOzCqhsbi696erqQldXF5XNzYWOVqiaNm8JDY0SmO8xG48fRePc2dMIXL8W/VX8WiQ6Ojro0q07Fi2Yhzu3w3H61Els3bwR/QcOFjra94Nnf5KbIJWKefPmYenSpRg+fDiysrJgYGAgnVORkpICDQ0NdOvWDe7u7kLE+6xujpWhrqaGvdNbybRfuBuHTgtOAMi5QF7K2/wnOrlvvY6FA+pi34ycx++5GI15u24UbuhCcub0KWRlZWF9wBqsD1gjsyzs738ESqV4Z//dzg3r1mDDOtntvHn7vkCphPMiOQn6pVkeLy6K+/5OTk4GAOjrF9/hX6pMT08PAYGb4eW5GAP69kaZMmXxy6gx6PXjT0JHK3RTprlj8YJ5+GXozyilVwpjXMahdZu2QseiIkwkEfBqTmlpabh//z4SExORlpYGLS0tmJqawtraukAThfT7blVgyqIjYXvx/IUhuwhWeRRBVEzPTFFMN7tIn8ihICQonhuuVkwP9OJ6nBfT3Q3t7+LCBvnTabFQaa+VdmaO0l6rMAlac9HR0YG9vT3atm2Lbt26oX379rC3t+eZB4iIiIiIPkEsFqNz5864di3vNbJev34NZ2dnHDhwQKb96NGjaN26Nezs7ODi4oIXL15Il0kkEvj4+KBBgwZwdHSEl5eX3BdCVJ2BXEREREREivAdz6nIyMiAm5sbHj58mO9yb29vJCQkyLSFh4dj1qxZcHV1RVBQEF69eiUzzWDTpk04evQo/P39sWLFCvzvf//Dpk2b5MrFTgURERERUREQERGBPn364MmTJ/kuv379Oq5evQpjY2OZ9u3bt6NDhw7o3r07rKys4OXlhXPnziEmJgYAsHXrVowfPx716tVDgwYNMGXKFOzYsUOubOxUEBEREREVAcHBwXByckJQUFCeZWKxGHPmzIGHh0eeK6aHhYWhXr160vvly5eHmZkZwsLCEB8fj9jYWNSvX1+63MHBAc+ePctT8fic73iKDBERERGRAJQ4e14sFue5wrmmpmaejgEA9O/f/5PPs3btWtjY2KBJkyZ5liUkJMDExESmzdDQEHFxcUj891IAuZcbGRkBAOLi4vI87lPYqSAiIiIiEkhAQAD8/f1l2lxdXTFu3Livfo6IiAjs3r0bR44cyXd5enp6nk7Kh8s5pKenS+/nXgYgT2fnc9ipICIiIiLKTYkXpRs1ahSGDh0q05ZfleJTJBIJZs+ejfHjx0srDB/T0tLK00EQi8XQ0dGR6UBoaWlJ/w3knKn1a7FTQUREREQkkE8Ndfpaz58/x82bN/HPP/9g6dKlAHKuBTd37lwcO3YMGzZsgKmpKZKSkmQel5SUBGNjY5iamgIAEhMTUbFiRem/AeSZ8P057FQQEREREeVWhK5IaGpqihMnTsi0DRo0CIMGDULXrl0BAHZ2dggNDUXPnj0BALGxsYiNjYWdnR1MTU1hZmaG0NBQaaciNDQUZmZmXz2fAmCngoiIiIioyNLQ0IC5uXmeNkNDQ2kVol+/fhg0aBDq1KmD2rVrY/HixWjevDkqVaokXe7j44Ny5coBAHx9fTFs2DD5cihgW4iIiIiIVIcS51Qog729PRYsWIAVK1YgNTUVjRs3xsKFC6XLhw8fjuTkZLi6ukJdXR29e/fGkCFD5HoNkUQikSg4t+D0+24VOoIgErYPFjqCILKzVe4Q/iqiIlSaVaRiutlQvU/qryNB8dxwtWJ6oBfX47yY7m5of8c/beu09Vbaa6WdmKq01ypM3/HuJCIiIiISQHHt6RWAatV2iIiIiIhI6VipICIiIiLKTcXmVCgD3zEiIiIiIioQViqIiIiIiHLjnAq5sVJBREREREQFwkoFEREREVFunFMhN75jRERERERUIKxUEBERERHlxjkVcmOlgoiIiIiICkQlKxUJ2wcLHUEQZeq7Ch1BEC+C/YWOQFToJJAIHUEQasX018Ks7OK5vyXFc7OhoV48j/PvGudUyI3vGBERERERFQg7FUREREREVCAqOfyJiIiIiOibcfiT3PiOERERERFRgbBSQURERESUWzE9SURBsFJBREREREQFwkoFEREREVFunFMhN75jRERERERUIKxUEBERERHlxjkVcmOlgoiIiIiICoSVCiIiIiKi3DinQm58x4iIiIiIqEBYqSAiIiIiyo1zKuTGSgURERERERUIKxVERERERLmIWKmQGysVRERERERUIKxUEBERERHlwkqF/FipICIiIiKiAmGlgoiIiIgoNxYq5MZKhYKJxWL07NYZIcHXhI5SIJolNHB970w4O1SXtvlM7YW0m/4yt9E/NZUu79PeAX8fmYvky34I8h0BQwNd6TLjMqWww2sY4s57I/qvJVg0vhvU1Yvu4ScWi7Fk0Xw4N6qPlk0bYcVvfpBIJELHKjRisRi9usse18+exmDUL0PQoH4d9OzaEZcvXRQwofK4jhmJOTNnCB2jUInFYvTu3gXXc+3vy5cuoE/PbmjgYIc+Pbvh4oXzAiZUDlX5PP+UhPh4THUbj+aNndCuVVP4enkiIyMDAHD37zv4ecBPaOxYF4MH/ITwsFvChlWgmCeP4TJ6OJo41UXHti2wdVNgvus0qm8nQDrliY+Px+SJ4+Hc0BGtWzjDe+l/+5/oWxTdb3XfoYyMDEyf6obIiIdCRykQLU0NbPUcgprVzGTaraqWx5wVh2HR2l1623L4CgCgXk1zrPEYgMXr/kCzn31goF8S6+YPkj5205Ih0C+lg+ZDfDFwWiD6tHeA28+tlbpdiuTluQhXr1zG6oBAeHr54uD+Pdi/N0joWIUiIyMDMz46riUSCSaNd4GhoRF27t6PTl26wW2iK2JjnwuYtPD9cex3XDh/TugYhSojIwPuUyfL7O8nTx5j8oRx6Nq9B/YdOoou3brDbbwLnj97KmDSwqUqn+efIpFIMNVtPNLT0hG4ZTs8vfxw/txZrPZfjhfJyRg9YiiqVf8B23fvQ9t2HTB25DCV+PvOzs7GBJdRKFOmLHbuOYCZc+Zhw/o1+OP3/0nXiYuLxQTX0Sr9BVsikWDKpPFIT0/Dpm074OWzDOfPnsGqlb8JHY2KMA5/UpDIiAi4T5tc5H+ttqpaDpuXDMn3mi9WVUyxbMtJxCe/zrNsdN+m2P/XDew8GgwAGD57C/45tgDmZoaITUxFQvIrLAo4hqiYJADAwVO30MjeslC3pbCkpqbg0MH9WLt+E2rXtgUADPp5GG6Hh6F3n74Cp1OsyMic4xofHdchwVcRExODLdt3Q6dkSVS1tETwtSs4dGA/xriMEyht4UpNScEyXy/UrFVb6CiFJjIyAjOnTcnzOZYQF4eevftg4OAhAIBBPw/FhoC1uHP7NswqVBQgaeFSlc/zz3kUHY3b4WH468xFGBoZAQDGuIzDMl8vGBoawqC0AWbOmQd1dXVUqVoVVy9fwr6gXRg3cbLAyQsmOTkJP1hZw332XOjqlkJlcws4OjXErZs30KFTF5w5fRKL53vAyNhY6KiF6lF0FMLDbuH0uUvS/T/WdTx8fZbCbcp0gdN9HzhRW36sVChI6PVg1Hd0wtadRfvXameHajgf8gDNf/aVadfT1UYF0zJ4+Dgh38c51q6CizcipPefxqcgJu4lnGwtIM58j2Gzt0o7FNZVy6FT09q4cL1o/gJ480YoSpUqhXr1HaVtw34ZifmLPAVMVThCQ3KO6y07ZI/r8LAwWNvYQKdkSWlbHXsHlRoi8TFfn6Xo3KUbLC2rCR2l0ISGhPy7v3fLtNdzdMLUGTMBAJmZmTi4fx/EmWLUqq2aHSxV+Tz/HCMjI/ivXS/9QvnBm9dv8OzpU1jb1IS6urq0vfoPNVTi79vY2AS/ei+Drm4pSCQS3Lp5AzdCQ+Dw7+f5xfPnMMZ1PKZMnylw0sJlaGSM1QEb8t3/RN+KlQoF6dO3v9ARFGL93vzHxVtVMUV2djam/9IO7RrbIDn1LVZsP4Md/8sZa1zOSB+xiakyj0lIfo0KJgYybSc2TICzQ3WE3n2CtUFFc0z206cxMDOrgP8dPoTADWuRmZmJbt174peRY6Cmplr99E8d10lJiTA2NpFpMzQ0RHx8nDJiKd21q1dw4/p17Dv0PyxeME/oOIWmT99+n13+5Mlj9OzSEVlZWRg/abJKVikA1fk8/xw9fX00auwsvZ+dnY2gXTvg6NQAZQ0N8eCf+zLrx8fFIiUlRckpC1fn9q0QF/sczk2bo1XrtgCAOfMWAgCuh6jmPJoP9PX10biJ7P7fvXM7nBo0EDDV94WVCvmp1jcgKjQ/VCkHiQR48Cge3cetweaDV7Bqdl90bZEz/KektiYyxO9lHpOR+R6amrL91sle+9D2l+XQKqGBLb8OVVp+RUp79w5PnjzGvr27MX+hJ9wmT8euHduwfetmoaMpTXpaGkpoasq0aWpqIlMsFihR4cnIyMCi+XPhPtsD2traQscRVJkyZbF99164z/bA2lUrcfKv40JHIgVZ7ueN+/fuwmX8RLRq3RZ3bofjwL49eP/+PS5fuoCzZ08jM1O1/r69/ZZj2co1ePDPffh6q16lWR7LfL1x795duE6YJHQUKsJYqaCvsuN/13Ds3G28fPUOAHDn4XNUNzfBiB+dceRMONLFmdD6qAOhVUIDaemZMm23HzwDAIyatx2XdkxD5fJl8ST2hXI2QkHU1TXw5s0beHr5wsysAgAgNu459uzehcFDhgmcTjk0tbSQ9tGvlmKxWCW/dK9d7Q+bmrVkftUrrvT09GBlbQMraxtERUZg947taN2mndCxqICW+/lg5/at+NXbD9Wq/wAAmD13Abx/XYwlC+fhhxpW+PGnfjJnA1MFNjVzhu+JMzIw230qJk2ehhIlNL/wKNWzzNcbO7ZtgZfPMlT/d/8TKxXfQrBORUhIyFevW79+/UJMQl/rQ4fig/tRcWhWP+cD6HlCKkwN9WWWmxrpIy4pFXq62mjX2Ab7/7opnfh4LyoWAGBUplSR61QYGRtDS0tL2qEAAAuLKoiPixUwlXKZmJgiMiJCpi0pKQlGHw2JUgV//vE7kpOS0KCePQBIf63968RxXL1+U8hoShMZ8RCpqamo61BP2lbVshquy/E5Tt+npUsWYt+e3Vjk6YVWuTqI3Xr0Queu3fHiRTKMjU3wm583zCpU+MwzFQ3JyUkID7uFFi3/O/tgVctqyMzMxJs3b1GmTPHqVHguXoi9Qbuw+FdvtG7LHwioYATrVCxYsAAR/34p+dwZNkQiEe7du6esWPQJc8Z0QgO7Kug02l/aZlujIh48igcABN+ORiN7S2z/d45FRVMDVDQ1wLXwRyipXQLblg5DTJwvroVHAwDqWlfG+/dZn5z4/T2ztbVDRkYGHj+KhrlFFQBAdFSUTCdD1dna2WFT4Dqkp6dLqxO3boaijr2DwMkUL3DzNrzP/G9o329+PgCAiW5ThIqkdOfOnsH/Dh/EgSPHpL/e3f37b1SpWlXgZFQQAWv8sX9vEDy9fNG6bXtpe0jwVezfuwe/evvB2NgEEokEly+cRy8VOLvd86dPMXXSOBw7cRYmpqYAgHt3/0aZMmVRpkwZgdMp19rV/ti3ZzeWevuhTbv2X35AMcNKhfwE61Ts378fbm5uePr0KYKCgqClpSVUFPoKx87dxtShbTFxUCscPhOG1g2tMKCzI9qPXAEgZ4L38fXjcS08GqF/P4bP1N44duFvPH6eDAA4dOoW/Kb/iLELdqJUSS2s9uiPNbvP4fXbdCE365tYVKkK56bNMWeWO2bNmYfk5ERsDFyHESPHCB1NaRzqOcK0XHnMne2OEaPH4vzZM7hzO1wlz4D1cWdRVzfnoo6Vzc2FiCOITp27YtOGdVixzBfde/XG1cuXcOzokTxniaKiIyoqEhsC1mDo8JGoU9cBSUmJ0mXm5lVw/twZ7A3ahYaNmmDblo149eoVunTrLlxgBbGpVRvWNjUxf+5MTJ7qjufPn2G5nzeGjRgtdDSlioqMxLq1qzHsl5Gwr+uApMT/9r+qn06XCo9gnQpNTU34+fmhT58++O233zB9Os+L/D0LvfsE/adtwJwxneAxthMeP3+BITM3SysP18Kj4bpoNzzGdEKZ0ro4deUexi7cJX38qHnb4TW5F46ucQUA7Pw9GLOXHxZkWxRhyVIfLF2yEEMH94O2tg769huAfgMGffmBKkJdXR2/rVyN+R6z0L9PT1SqbA6/5atQvrzZlx9MRY5puXJYFbABPks9sXvndpQ3qwAvv+WwtqkpdDT6RudOn0JWVhY2rFuDDevWyCy7cfs+lvoswzIfLyzz9UJtWzusWb8JJUvqCpRWcdTV1eG3fBWWei7CkEF9oaOjg779Bxarz28AOPPv/l8fsAbrA2T3f9jf/wiU6jvDQoXcRBKBr+4TGRmJ4OBg9Ov3+VMZyiP9/ZfXUUVl6rsKHUEQL4L9v7wSqYziWpHOVuELsX2OWjHd4VnZxXN/F9PDHBrqxfM41/6OTxdUuv82pb1W6k7V6NQKvjstLS1haVk0r6xMRERERKqHcyrkx+tUEBERERFRgQheqSAiIiIi+p6wUiE/ViqIiIiIiKhAWKkgIiIiIsqFlQr5sVJBREREREQFwkoFEREREVEurFTIj5UKIiIiIiIqEFYqiIiIiIhyY6FCbqxUEBERERFRgbBTQUREREREBcLhT0REREREuXCitvxYqSAiIiIiogJhpYKIiIiIKBdWKuTHSgURERERERUIKxVERERERLmwUiE/ViqIiIiIiKhAWKkgIiIiIsqNhQq5sVJBRERERFSEiMVidO7cGdeuXZO23bp1C3379oW9vT3atWuHvXv3yjzm8uXL6Ny5M+zs7DB48GDExMTILN+8eTOcnZ1hb2+PmTNnIi0tTa5M7FQQEREREeUiEomUdpNXRkYG3Nzc8PDhQ2lbYmIiRowYAUdHRxw8eBDjx4/HwoULcfbsWQDA8+fP4eLigp49e2Lfvn0oW7Ysxo4dC4lEAgA4fvw4/P39sWDBAmzZsgVhYWHw9vaWKxc7FURERERERUBERAT69OmDJ0+eyLSfPHkSRkZGcHNzg4WFBTp16oTu3bvjf//7HwBg7969qFWrFoYNG4bq1avD09MTz549Q3BwMABg69at+Pnnn9GiRQvY2tpi/vz52L9/v1zVCnYqiIiIiIhyUWalQiwW482bNzI3sVicb67g4GA4OTkhKChIpt3Z2Rmenp551n/z5g0AICwsDPXq1ZO26+jooGbNmrh16xaysrJw+/ZtmeV16tRBZmYm7t+//9XvmUpO1P63klPsJAevFDqCICqN2C10BEE8XveT0BEEwblzxUt2Mf1AL65nsyymu7vYbjflCAgIgL+/v0ybq6srxo0bl2fd/v375/scFStWRMWKFaX3k5OT8fvvv0ufIzExESYmJjKPMTQ0RFxcHF69eoWMjAyZ5RoaGjAwMEBcXNxXb4dKdiqIiIiIiL6VMq9TMWrUKAwdOlSmTVNT85ufLz09HePGjYORkRF++innB8i0tLQ8z6mpqQmxWIz09PR8X/PD8q/FTgURERERkUA0NTUL1InI7e3btxg7diwePXqEnTt3QkdHBwCgpaWVp4MgFouhr68PLS0t6f2Pl394/NfgnAoiIiIioly+57M/fcqbN28wfPhwPHz4EFu2bIGFhYV0mampKZKSkmTWT0pKgrGxMQwMDKClpSWz/P3790hJSYGxsfFXvz47FURERERERVh2djZcXV3x9OlTbNu2DdWrV5dZbmdnh9DQUOn9tLQ03L17F3Z2dlBTU0Pt2rVllt+6dQsaGhqwsrL66gwc/kRERERElFsRO1nCvn37cO3aNaxZswb6+vpITEwEAJQoUQIGBgbo1asXAgMDsW7dOrRo0QKrVq1CxYoV4eTkBCBnAriHhwd++OEHmJiYYN68eejTp49cw5/YqSAiIiIiKsKOHz+O7OxsjBo1Sqbd0dER27ZtQ8WKFbFy5UosWbIEq1atgr29PVatWiUdftWpUyc8e/YMHh4eEIvFaNu2LaZOnSpXBpFEononMkvLFDqBMCRQuV35VSqPCPrySiqo2J5Stoj9ekREXy87W+gEwlBXK54fbDolhE7waWajDyjttZ6v7am01ypMrFQQEREREeWizFPKqgpO1CYiIiIiogJhpYKIiIiIKBdWKuTHSgURERERERUIKxVERERERLmwUiE/ViqIiIiIiKhAWKkgIiIiIsqNhQq5sVJBREREREQFwkoFEREREVEunFMhP1YqiIiIiIioQFipICIiIiLKhZUK+bFSQUREREREBcJOhQIcPnQAdWrVyHOzr20ldLRCIxaL0bt7F1wPviZt8/JcDPtaVjK33Tu3C5hSfpoaariwqD0aW5lI2xwsDXFsVms8WtsLVz07YmDTqjKPGdOuBm75dsGTgN7YM7kZqpqWyve5lw2tj2ndaxVqfkVLiI/HVLfxaN7YCe1aNYWvlycyMjIwd9YM1K1tlec2cvjPQkdWKFU9zr/k4+32mDUjzzbb17LCyGGqv78/eP36Ndq2bIojhw4IkKxwFbftjnnyGC6jh6OJU110bNsCWzcFSpddvnQBfXt3Q6P6dujbuxsuXTgvYNLCUxy/t8hLJBIp7aYqOPxJAdq174jGTZyl999nvseI4T+jabPmwoUqRBkZGZg5bQoiIx7KtEdFRmLcRDd07d5D2qarm/8X7O+RVgk1BIxqCOuKBtI2k9LaCHJrhk2nI+C64SrsLMpixXBHxKem4a+wWPRuaI4p3Wpi1NoriIp/jWnda2HHxKZo6H5M5rnHdbDCoGaW8Dp0R8lb9e0kEgmmuo2Hvn5pBG7ZjtTUVMz3mAU1dXVMmTEL4yZNlq77/NkzjBw2GP36DxIwsWKp6nH+Jflt99QZszD+o/09Yuhg9Bug+vv7g+V+PkhMSFByqsJX3LY7OzsbE1xGwaZWbezccwBPnjzGzOmTYWxiglq1bTFl0ji4jJuIZi1a4ezpk5g80QUHjvwBswoVhY6uUMXtewspBzsVCqCtrQ1tbW3p/cD1AYBEggmTpgiYqnBERkZg5rQpkEgkeZZFR0fi56HDYGRkLECygvnBTB8BoxvmOS11x7oVkJCahsX7wwEAUfFv0MTKBL0amOOvsFjo6ZTA/KAwnAyPBQCs+P0ezi/qACM9LSS9zkApbQ2sGO4EZ2sTPE1+q+StKphH0dG4HR6Gv85chKGREQBgjMs4LPP1wqTJ06Cnpydd12PWDLRu2x4tWrUWKq5Cqepx/iWf2m49PT3Z/T1zBtoUk/0NADdvhCL42lWV2+fFcbuTk5Pwg5U13GfPha5uKVQ2t4CjU0PcunkDxiYm6NmrDwYMGgIAGDh4KALXrcWdO7dVrlNRnL63fCtVqiAoC4c/KVhqago2bVyP8ZMmQ1NTU+g4ChcaEoL6jk7YsmO3TPubN2+QEB8PcwsLYYIVUOMaJrh0LwEdFp2UaT91Ow7jAoPzrK+vUwIAsOl0BLaeiwQA6OmUwPBW1XHvaQqSXmcAAMyNS0G7hBpazjuBx4lFq1NhZGQE/7XrpR2KD968fiNz/9rVK7gZeh2uEyYpM16hUtXj/Es+td25Xbt6BTeKyf4GcoYGLZw7B+6z5qCEZgkB0hWe4rjdxsYm+NV7GXR1S0EikeDWzRu4ERoCh/qOqFffCVOmzwQAZGZm4tCBfRCLxahVq7bAqQuXqn9vIeVhpULB9uzeBRNjE7Rp217oKIWiT99++bZHR0VCJBJhw7oAXLp4HqVLG2Dgz0PQtVuPfNf/3mw6E5Fve0zSW8Qk/dcZMNLTQg+nynmGMfV3roIVw52QnpmFPj5npe1/x6Sg/28XCiVzYdPT10ejxv+Vx7OzsxG0awccnRrIrLc5cD26dOuBcuXKKztioVHV4/xLPrXduW3a8O/+Lq/6+xsAAtetRQ1razRs3ESJiZSjuG73B53bt0Jc7HM4N22OVq3bSttjnjxGr24dkZWVhXETJ6tcleJjqv695ZuxUCE3QSoVYrEY3t7eaNasGerWrQtXV1dERkbKrJOUlARra2sh4n0ziUSCgwf2om//gUJHUbro6CiIRCJYVKmClavXoUev3lg0zwOnT/4ldDSF0S6hjk2uTZCQmo4tZ2WP13N/x6OFx5/Ydi4S2yY4o7KRrkApC89yP2/cv3cXLuMnStuexsQgJPhqsTnmi8Nx/jnS/T2geOzvyMgI7NsThCnT3IWOolTFZbu9/ZZj2co1ePDPffh6e0rbDcqUxdadezF9pgcCVq/Eqb+OC5iycBXn7y2keIJUKvz8/HDmzBlMmzYNEokE27dvR69eveDj44PWrf8bo/upcZ7fq7/v3EZCfDzad+gkdBSl69K1O5o1b4HSpQ0AAD/UqIHHjx9hb9AutGzdRthwCqCrpYFtE5xhWU4PnZecRJo4S2b5sxfv8OzFO7hvv4HGNUzQt0mVIjUp+0uW+/lg5/at+NXbD9Wq/yBtP3XyBH6oYYWqltUETKc8qn6cf8mpv06ghpUVLIvB/pZIJFg4dw7GuI7LMwRQlRWn7bapmTOsSZyRgdnuUzFp8jSUKKEJPT09WFnbwMraBtFREQjatR2t2rQTOG3hKM7fW76EcyrkJ0il4o8//sCSJUvQqVMndO7cGbt27UK/fv0wceJE/PHHH9L1itoOvXzpAuo61IN+6dJCR1E6kUgk/aL1QdWqlkhQgbOGlNLWwJ4pzWBVoTR6LD2NqPj/5hQ0sTJBtXJ6Mus/iH2FsqVUZ1zq0iULsX3rJizy9MrzH+vlSxfQoqVqTNb9Gqp8nH+Ny5cuoHkx2d+xsc8Rdusm/Ly90Kh+XTSqXxdxsbFYvGAeXEaPEDpeoVH17U5OTsKZ07Jz56paVkNmZibCw27hZuh1mWVVqlZDyssUJSZUruL8vYUUT5BKRXp6OgwMDKT3RSIRpk+fDjU1NUydOhUaGhqwt7cXIlqB3A4PRx37ukLHEMRq/xUIu3UTARs2Sdv+uX8PFlWqCJiq4EQiYMu4JrAwLoWuv55CROxrmeXjOlnjadJbTN6S8x+RmkiE2pUNEPDXAyHiKlzAGn/s3xsETy9ftP5ovK1EIsHdO7cxfMRogdIpn6oe519DIpHg7zu3MXxk8djfJiamOHxMdtjLh9PoduzURaBUhU/Vt/v506eYOmkcjp04CxNTUwDAvbt/o0yZsggPu4X/HT6I/YePSX/UvH/vb1hUrfq5pyzSivP3FlI8QSoVTk5O8PLywosXL2Tap06dip9++gmTJk3Czp07hYhWIBERD1G1quoPC8hPs2YtcON6CLZuCkTMkyfYs3sXjh45jMFDhgkdrUAGNq2KJtYmmLgpGK/eZcKktDZMSmvDQDenErHp1EP0bVIFvRqYo1o5Pfj8XA/aJTQQdPGRsMEVICoqEhsC1mDIsBGoU9cBSUmJ0hsAxD5/hrdv36KqpaXASZVHVY/zr1Hc9reGhgYqVzaXuamrq6Ns2bLSL6OqSNW326ZWbVjb1MT8uTMRFRmBixfOYbmfN4aNGI2OnboiKSkRK3/zxZPHj7Bn9w4cO3oEQ4ePFDp2oSnO31u+hBe/k58glYpZs2Zh/PjxaNy4MTZs2IDGjRtLl82ZMwdlypTBmjVrhIhWIC+Sk6BfWl/oGIKoWbs2vPyWY43/Cqz2XwEzswpYstQHdnWKXsUpt871KkFdTQ27JjWTab90PwHdfj2NP289x9St1zGte02YlS2J6xHJ6O1zFm8z3guUWHHOnT6FrKwsbFi3BhvWyf493rh9H8nJyQAAff3iUzZX1eP8axTH/U2qR11dHX7LV2Gp5yIMGdQXOjo66Nt/IPoNGASRSIRVazbAx8sTu3dth5lZBSz1WQ5rm5pCxy40xfl7CymeSCLgbOioqCgYGxvLXFTpg8jISJw6dQojR8r/C0FapiLSFT0SFK2J7YpSeUSQ0BEE8XjdT0JHEIQK/ahDRB/JzhY6gTDU1YrnB5vOd3wJlGpT/vjySgoS4dNBaa9VmAS9TkXVz4xTtLS0hGUxKbMTERERERVlvPgdEREREVEuqjTXQVkEmahNRERERESqg5UKIiIiIqJcWKiQHysVRERERERUIKxUEBERERHlwjkV8mOlgoiIiIiICoSVCiIiIiKiXFiokB8rFUREREREVCCsVBARERER5aJWTK9yXhCsVBARERERUYGwUkFERERElAvnVMiPlQoiIiIiIioQViqIiIiIiHLhdSrkx0oFEREREREVCDsVRERERERUIBz+RERERESUC0c/yY+VCiIiIiIiKhBWKoiIiIiIcuFEbfmxUkFERERERAXCSgURERERUS6sVMiPnQoq8h4F/CR0BEFUGb1X6AiCeLKuj9ARBPE+SyJ0BEGoFdN6ena20AmEIZEUz+M8u3huNgB+cVcl7FQQEREREeXCQoX8iulvQEREREREpCisVBARERER5cI5FfJjpYKIiIiIiAqElQoiIiIiolxYqJAfKxVERERERFQgrFQQEREREeXCORXyY6WCiIiIiIgKhJUKIiIiIqJcWKiQHysVRERERERUIKxUEBERERHlwjkV8mOlgoiIiIiICoSVCiIiIiKiXFiokB8rFURERERERYhYLEbnzp1x7do1aVtMTAyGDBmCOnXqoGPHjrh48aLMYy5fvozOnTvDzs4OgwcPRkxMjMzyzZs3w9nZGfb29pg5cybS0tLkysROBRERERFREZGRkQE3Nzc8fPhQ2iaRSODi4gIjIyPs378f3bp1g6urK54/fw4AeP78OVxcXNCzZ0/s27cPZcuWxdixYyGRSAAAx48fh7+/PxYsWIAtW7YgLCwM3t7ecuVip4KIiIiIKBeRSKS0mzwiIiLQp08fPHnyRKb96tWriImJwYIFC2BpaYlRo0ahTp062L9/PwBg7969qFWrFoYNG4bq1avD09MTz549Q3BwMABg69at+Pnnn9GiRQvY2tpi/vz52L9/v1zVCnYqiIiIiIiKgODgYDg5OSEoKEimPSwsDDY2NihZsqS0zcHBAbdu3ZIur1evnnSZjo4OatasiVu3biErKwu3b9+WWV6nTh1kZmbi/v37X52NE7WJiIiIiHJR5kRtsVgMsVgs06apqQlNTc086/bv3z/f50hMTISJiYlMm6GhIeLi4r64/NWrV8jIyJBZrqGhAQMDA+njvwYrFUREREREAgkICICDg4PMLSAgQK7nSEtLy9MJ0dTUlHZWPrc8PT1dev9Tj/8arFQQEREREeWizIvfjRo1CkOHDpVpy69K8TlaWlpISUmRaROLxdDW1pYu/7iDIBaLoa+vDy0tLen9j5fr6Oh8dQZWKhTkRXIypkwajyYN66FLhzY4fOiA0JEKlVgsRu/uXXA9+L9TmcXGPofrmJFoWK8OunZoixN//iFgQsWKefIYLqOHo4lTXXRs2wJbNwXmWef169do37opjhwuevteU0MN5xa0Q6MaxtI2h6pl8fvMlohe3QOXl7THAOcqMo/p28QClxa3R/TqHvhjdis4VjPM97l9f66Hqd1qFmp+ZXIdMxJzZs4QOkah+NxxfvnSBfTt3Q2N6tuhb+9uuHThvIBJC0d+n2vhYbfw84C+aFS/Lrp3bo8D+/YKmFCxPre/vX9dDAdbK5lb0K7tAqYtHBNcR2HeHHfp/Yvnz6J/nx5wbuCAvr274dzZ0wKmU6yE+HhMdRuP5o2d0K5VU/h6eSIjIwNzZ81A3dpWeW4jh/8sdORiQ1NTE6VKlZK5ydupMDU1RVJSkkxbUlKSdEjTp5YbGxvDwMAAWlpaMsvfv3+PlJQUGBsb42uxU6EAEokEkya4ID4+Dhs2bsXU6TPh6/UrTv11QuhohSIjIwPuUycjMuK/U5m9f/8e48eOgoaGBnbtPYDBQ4dh1oxpiHj4QMCkipGdnY0JLqNQpkxZ7NxzADPnzMOG9Wvwx+//k1lv5W8+SExIECjlt9PSUEPAqAawrlha2mair41dk5ri0v1EtJz3F7wO/Y0lA+zR2rY8AKBFrXL4dUBd+P3vLlrO+wtn/47HzonOMDXQlnlu1/Y1MKhZVaVuT2H649jvuHD+nNAxCsXnjvOYJ48xZdI4dOnWA3sOHkXnrt0xeaILnj97KnRshcnvcy0pKRGuY0aiXn1H7Np3AKPHjoOX5yJcOHdWuKAK8qXPteioSLhOcMPx0xekt67dewmcWrGO//G7TOf44YN/MNVtPLp264mdew6gZ+8+mD55Ih788/UTVb9XEokEU93GIz0tHYFbtsPTyw/nz53Fav/lmDJjFk6cuSC9bd6+G5qamujXf5DQsQUlEinvpgh2dnb4+++/pUOZACA0NBR2dnbS5aGhodJlaWlpuHv3Luzs7KCmpobatWvLLL916xY0NDRgZWX11RnYqVCAu3/fQditm/Bc6gsraxs0bd4CQ4f/gi2b8/6aXdRFRkZgcP+fEBMjeyqzixfOIy4uDos8vWBRpSp69+mLJk2bIuzWTYGSKk5ychJ+sLKG++y5qGxugSbOzeDo1BC3bt6QrnPzRiiCr12FodHX9+i/Bz+Y6eOP2a1gYVJKpr1D3QpISE3HkgO3EZ3wBoeCY7D38mP0cqoMAOjb2AJ7Lj/C/qtPEJ3wBksP3kHCq3S0sTUDAJTS1kDg2IYY19EKT5PfKn27CkNqSgqW+XqhZq3aQkcpFJ87zuPj49CzVx8MGDQEFStWwsDBQ6GjUxJ37twWOrZCfOpz7cypUzAyNMK4iW4wN7dA+46d0LlrN/xx7KhASRXnS59r0VGRsLK2gZGRsfQmzzCI711qagpWLPOBTc3//p7/PHYU9R0boO+AQahU2Rx9+g5AvfqO+OvEnwImVYxH0dG4HR6GeQuXwLJaddR1qIcxLuPw57Gj0NPTk9nPa1evROu27dGiVWuhY5McHB0dUb58ebi7u+Phw4dYt24dwsPD0bt3bwBAr169cOPGDaxbtw4PHz6Eu7s7KlasCCcnJwA5E8ADAwNx8uRJhIeHY968eejTpw+HPynb06cxKFO2LCpWqiRtq/5DDdz9+w4yMzMFTKZ4oSEhqO/ohC07dsu0Xw+5BkenBihV6r8vp8tWrEKvH39SdkSFMzY2wa/ey6CrWwoSiQS3bt7AjdAQONR3BJAzZGLR/DmYPnMONDVLCJxWPo1+MMbF+4nouPiUTPvp27GYsDE4z/p6JXO2z/+P+1hzPG8VSl8nZ7m5sS60Sqij9fy/8DhRNToVvj5L0blLN1haVhM6SqH43HFer74TpkyfCQDIzMzEoQP7IBaLUUtFOlif+lxr3KQJ5i1akmf9N69fKytaofnc/n7z5g0SEuJhbm4hdMxC85uvNzp27oqqlpbSts5du8N1gluedVVhfxsZGcF/7XoYGhnJtL95/Ubm/rWrV3Az9DpcJ0xSZrzv0vd6nYpPUVdXx+rVq5GYmIiePXviyJEjWLVqFczMcn7sq1ixIlauXIn9+/ejd+/eSElJwapVq6Sv36lTJ4waNQoeHh4YNmwYbG1tMXXqVLkyfFcTtd+/f483b97AwMBA6ChyMTQ0wutXr5GWlibt0cXFxf27Pa9RpkxZgRMqTp++/fJtf/b0KczMKmD5Ml/8/r/DMDAogzEu41Tul47O7VshLvY5nJs2R6vWbQEAG9evRQ0razRs1ETgdPLbfDYy3/aY5HeISX4nvW+kp4XujpXgffguAOD2kxSZ9VvUKodq5fRx4V48AODvmFQMXH6xcEIL4NrVK7hx/Tr2HfofFi+YJ3ScQpffcQ7kjMHv1a0jsrKyMG7iZJhVqChgSsX51OeaWYWKMtv4IjkZx/84hlFjXZUVTSk+3t93/74DkUiEwPUBuHzxPEobGGDAoCHo0q2H0FEVIuTaVdy8cR279x3Gr4vnS9urVLWUWS8y4iFCgq+qxI9jevr6aNTYWXo/OzsbQbt2wNGpgcx6mwPXo0u3HihXrryyI9I3+Oeff2Tum5ubY/v2T899atasGZo1a/bJ5SNHjsTIkSO/OY9glYrff/8dCxYswPHjxyGRSLBo0SLUrVsXDRs2ROPGjT/7pnxvatvawdjEBEuXLETau3d48uQxtm/dBAAqV6n4lHfv3uHI4YN4/SoVy/3XoHPXbpjqNgF/q8jwiA+8/ZZj2co1ePDPffh6eyIqMgL79wZh8jT3Lz+4iNIuoY6NLo2Q8CodW8/l7YRYGOti5fD62HflcZ7OhirIyMjAovlz4T7bQ3oWDVX38XH+gUGZsti6cy+mz/RAwOqVOPXXcQFTKld6ejqmTBoPQyMjlfiSmdvH+/tRdBREIhEsqlTBitXr0L1nbyxe4IHTp/4SOmqBZWRkYMmiuZjuPuezf88pL19i2uQJsKtjj2YtWikxoXIs9/PG/Xt34TJ+orTtaUwMQoKvom//gcIF+44UtTkV3wNBKhWBgYFYs2YNGjZsiLlz5+LQoUO4d+8evL29Ua1aNdy+fRs+Pj549+5dgXpMyqKlpQVv398wbcpENG7ggLJlDfHzsF/g6+UpMxxIlWmoq8OgtAFmzpkHNTU1WNvUxM0boTiwb49KjUH/MP5WnJGB2e5TcffObYx2GQdDQ6MvPLJo0tXSwJZxjWFpqofOnqeRJs6SWV7VtBT2TWmGRwlv4bb5ukApC9fa1f6wqVkLjZs4f3llFfHxcT5p8jSUKKEJPT09WFnbwMraBtFREQjatR2t2rQTOG3he/fuLSaNc8HjR4+wcdsOlZpbAOTd3+evhKJp8xYoXdoAQM5w3iePH2Hfnl1o2aqNgEkLbv3aVbC2qYWGjT9dWU5OToLLqOGQZGdjqc9yqKmp1kjx5X4+2Ll9K3719kO16j9I20+dPIEfalihqooO8aTCJ0inYseOHfDz80PTpk0RGhqKgQMHYu3atdKSjKWlJcqUKYM5c+YUiU4FANSqbYtjx08jKSkRBgZlcOXyJZQpUwYlS+oKHU0pjIyNAZFI5sPXwqIKHjz45zOPKhqSk5MQHnYLLVr+N5SrqmU1ZGZm4nZ4GCIePsQyHy8AQHp6GjwXzsNff/6BlWvWCxVZIUppa2D3pKaoYlIKPb3PIjpBduxtDTN97J/aDI8T36LvsvNIz8z6xDMVbX/+8TuSk5LQoJ49ACAzM+c83n+dOI6r14v+iQg++NxxHh52C2oiNdg71JMuq1K1GkJDQoSIqlRv3ryB6+gRiHnyBOs2blaZeQaf299v375FmTJlZNa3qGKJkFyn2i2qTvx5DMnJSXBu4AAAEP/793zqrxO4cDUUCfHxGD1iCAAgIHArypRVneHLALB0yULs27Mbizy98vwgcPnSBZnjobhT5nUqVIUgnYqXL1/CwsICAODg4IDy5cvD6KPJQxUrVkRaWpoA6eSXmpqCCa5j8NvK1TD69+w/F86flU7kLQ5q29phQ8BaZGVlQV1dHQAQFRUJM7MKAicruOdPn2LqpHE4duIsTExNAQD37v4Nff3S2Lpzj8y6I4cNRt/+g9ChUxchoiqMSARsdm0Mc2NddFt6BhFxshMVTUprY8/kpoiKf4N+yy7gbcZ7gZIWvsDN2/A+87/t+83PBwAw0W2KUJEKxaeO8zJlyiI87Bb+d/gg9h8+Jv2P9v69v2FRVXVOF5yf7OxsTJ44Ds+ePsWGzdtQRYW293P7e/fObQi/dRNr1m+Srv/gn3uwsKjyqacrMgICt+D9+//+nlf85gsAGD9xMtLevcO4sSOgpqaGtRs2S/8/VxUBa/yxf28QPL180bpte5llEokEd+/cxvARowVKR6pAkJpe3bp1sWrVKrx7lzMR9PTp06hZ87+LYyUkJMDT0xMNGzYUIp7cSpc2wLt37/CbrzeexsTgwL69OHxwP4YM/UXoaErTvmNnZEuy4bloPp48eYw9u3fi8sUL6Nn7R6GjFZhNrdqwtqmJ+XNnIioyAhcvnMNyP2+MGO2CSpXNZW7qGuooY1hW+p90UTXAuSoaWxlj0ubrePUuEyb62jDR14aBbs7FeOb3sYO6mggTN4VAV0tDulxX67s694NCmJlVQGVzc+lNV1cXurq6qGxuLnQ0hfrUcT5sxGh07NQVSUmJWPmbL548foQ9u3fg2NEjGDq8aFSSv9WhA/twPfgaPOYvhJ6+HpKSEpGUlIjU1BShoxXY5/Z302YtEBoagq2bAxET8wR7g3bh9/8dxqAhw4SOXWDlzSrIfGZ/+HuuVNkcGwPX4enTGMxbmDOP6MP+VoWzP0VFRWJDwBoMGTYCdeo6SLctKSkRABD7/Bnevn0rczas4q6onf3peyDIN4C5c+di5MiRmD17Nvz8/GSWnTx5EuPGjUOtWrXg6en5iWf4/nj5LMPC+XPRu2cXVKhQEd6+y1Grtq3QsZSmVKlSWLN+I5YsnIcfu3dBeTMz/OrtB2ubon8lZXV1dfgtX4WlnoswZFBf6OjooG//geg3QHUvDNTZoQLU1dSwc6LsPIJL9xPQw+ssOtStgJJaGrjq2VFmuffhv+F9+G9lRiUF+dxxLhKJsGrNBvh4eWL3ru0wM6uApT7LVeLv+3NO/XUC2dnZGO8i++utQ7362LB5m0CpFONL+9vLdznWrlqBtatWoLxZBSz+1Qe2dvZCxy5Up0+eQEZ6OoYMlJ2I37lrd2lHo6g6d/oUsrKysGHdGmxYt0Zm2Y3b95GcnAwA0Ncvnd/Dib6KSCKRSIR4YYlEIr08eG7Jycl4+vQpateu/c2To9KKxwmX8pBAkF0puOxsoRMIo+qYvUJHEMSTdX2EjiCI91nF8+9bxebIfrXi+rkm0FcSwampqc6v1fLQ1fx+t7vZsktKe61zkxor7bUKk2BjFUQiUZ4OBQAYGhrC0NBQgERERERERPQtiulvQEREREREpCiqN6uSiIiIiKgAVGkCtbKwUkFERERERAXCSgURERERUS4sVMiPlQoiIiIiIioQViqIiIiIiHLhnAr5sVJBREREREQFwkoFEREREVEuLFTIj5UKIiIiIiIqEFYqiIiIiIhyUWOpQm6sVBARERERUYGwUkFERERElAsLFfJjpYKIiIiIiAqElQoiIiIiolx4nQr5sVJBREREREQFwkoFEREREVEuaixUyI2VCiIiIiIiKhBWKoiIiIiIcuGcCvmxUkFERERERAXCSgURERERUS4sVMhPJTsVxfVAEKGYbriaROgEgngc0EfoCIKoPvGw0BEE8WBZN6EjCEKC4vn3rV5MZ4lmF8/djfdZxXTDi+v3FhXF4U9ERERERFQgKlmpICIiIiL6VsV29EcBsFJBREREREQFwkoFEREREVEuxXRaU4GwUkFERERERAXCSgURERERUS68+J38WKkgIiIiIqICYaWCiIiIiCgXFirkx0oFEREREREVCCsVRERERES5qLFUITdWKoiIiIiIqEBYqSAiIiIiyoWFCvmxUkFERERERAXCSgURERERUS68ToX8WKkgIiIiIqICYaWCiIiIiCgXFirkx0oFEREREREVCCsVRERERES58DoV8mOlgoiIiIiICoSdCiIiIiIiKhB2KhTkyePHGD1iOBrUs0e7Vs2xeeMGoSMpRXHbbrFYjN7du+B68LU8y16/fo22LZviyKEDAiRTDrFYjCWL5sO5UX20bNoIK37zg0QiETpWgWhqqOHkzBZoUN1Qpt3CSBcP/TrnWb9BNUP8OaM5Hvh1wuHJzrCuoC+zfEZXG9zybI/bSztgZjebIj3ZTxX39+fk9/cdHnYLPw/oi0b166J75/Y4sG+vgAkLh1gsRq/unRGSa7ufPY3BqF+GoEH9OujZtSMuX7ooYELFSoiPx1S38Wje2AntWjWFr5cnMjIyZNZ5/fo12rVSrc/zM6f/gmMda5nbjCkTMHr44DztjnWssXDuLKEjC0qkxJuq4JwKBcjOzobr2JGoWas2gvYfxJPHjzFjqhtMTEzRsXMXoeMVmuK23RkZGZg5bQoiIx7mu3y5nw8SExKUnEq5vDwXITj4GlYHBOLd27eYMXUSzMzM0LtPX6GjfRMtDTWsHOKAGmayHYPyBtrYNNoJ2prqMu2VDEti69gGWP1XBA5df4rRrashcKQTmi04icwsCUa2tET3ehUwYn0wNNTVsOLnukh+k4GAU5HK3CyFUbX9/Tn5/X0nJSXCdcxI/NinLxYs+RX3/v4b8+bMhLGxMZybNRcurAJlZGTAfdpkme2WSCSYNN4F1ar/gJ279+PM6ZNwm+iKg0eOoXx5MwHTFpxEIsFUt/HQ1y+NwC3bkZqaivkes6Cmro5Jk6dJ11uxTPU+z6MjI+HcrAXc58yXtmlpaiFbko3MzExp29+3wzFz2iT06tNPiJhUhLFSoQDJyUmoYWWN2R7zYG5uAeemzeDYoCFu3ggVOlqhKk7bHRkZgcH9f0JMzJN8l9+8EYrga1dhZGSs5GTKk5qagkMH98Nj3kLUrm0LpwYNMejnYbgdHiZ0tG9SvZweDk9pCnMjXZn2drblcGx6c4jfZ+d5zNBmVXDz0Uv89sc/eJT4FvP23UF2tgTVyukBAIY1rwrf3+8jJOoFrjxMwpLDd/Fz06pK2R5FU7X9/Tmf+vs+c+oUjAyNMG6iG8zNLdC+Yyd07toNfxw7KlBSxYqMjMCg/n3w9KPtDgm+ipiYGMyZuwBVLS0xfMQo2NnVwaED+wVKqjiPoqNxOzwM8xYugWW16qjrUA9jXMbhz1z7VFU/zx9FR8HSsjqMjIylNz19fZQubSC9X6ZMWaxeuQyDhgyHTc1aQkcWlEgkUtpNVXxVpcLKyuqrN/revXsFClQUGRubwNv3NwA5v4LcunkDN66HYOacucIGK2TFabtDQ0JQ39EJLuMnolF9e5llYrEYC+fOgfusOVg430OghIXv5o1QlCpVCvXqO0rbhv0yUsBEBdOgmiGuPEjC0v/dw8Nl/w1zalnTFD5H7yEy4Q32Tmgi+5jqRthz9b8vYOmZWWgy/yQAwLS0NiqULYlrEcnS5SGRyahkWBIm+lpIeCU7vOJ7p2r7+3M+9ffduEkT1LCyyrP+m9evlRmv0ISGBKO+oxNcx09Cw/p1pO3hYWGwtrGBTsmS0rY69g4ID7ul/JAKZmRkBP+162FoZCTT/ub1GwD/fp7Pm4MZs+ZgkYp9nkdHRcDRqeFn1zl65CBevUrF4KG/KCkVqZKv6lRs3bq1sHOojA5tWiI29jmaNmuB1m3aCR1HaVR9u/v0/XQZOHDdWtSwtkbDxk0+uY4qePo0BmZmFfC/w4cQuGEtMjMz0a17T/wycgzU1Ipe0XPbxUf5tk/flfNL/MdzLACgsqEu0sRZWDOsHpyqGeJB7GvM2XsbD+New0RfCwAQn5ouXT/pdU5HoryBTpHrVKja/v6cT/19m1WoCLMKFaX3XyQn4/gfxzBqrKuyohWqPn3759uelJQIY2MTmTZDQ0PEx8cpI1ah0tPXR6PGztL72dnZCNq1A45ODQAAgevXwsrKGg0bqdbnuUQiweNHj3D1ykVsCgxAdnY2WrVph1Fjx6FECU3pOls3bUDfAYNRsqTuF55R9ampTgFBab6qU+Ho6Jin7c2bN3jy5AmqVasGsViMUqVKKSRQ3bp1cfjwYVSqVEkhz6dsvr+tQFJSEhYvnAfvpZ6YMXO20JGUorhud2RkBPbtCcKeA4eFjlLo0t69w5Mnj7Fv727MX+iJpMRELFrgAW1tHQweMkzoeEqhq6WOmd1ssOyPf7DqxEMMa1EVu8Y1QtP5J6Hz7/yLjFzDpj78W1Oj6H0J5/6WlZ6ejimTxsPQyAi9fvxJ6DiFKj0tDSU0NWXaNDU1kSkWC5So8Cz388b9e3exbddeREVGYP+eIATtV73P87jY50hPT0OJEprw9FqGZ8+fwnfpEmSkZ2Dy9JkAgNDrwUhIiEf3nj8KnJaKKrknaovFYixYsAAHDuScEeH48eNYunQp0tLS4Ofnh9KlS3/xOdzd3T/7/N7e3tDVzekle3p6yhtRUDVr1QYAiDMy4D59CiZPmZbnw1kVFcftlkgkWDh3Dsa4jstTSldF6uoaePPmDTy9fGFmVgEAEBv3HHt27yo2XzLfZ0tw8k48Np+LBgBM33kL1xa2RVvbcohOeAsgZ/L3h86E1r+diTRxljCBC4D7+z/v3r3FpHEuePzoETZu2wEdHR2hIxUqTS0tpKWkyLSJxWJoa2sLE6iQLPfzwc7tW/Grtx8sq1XHsMH9MdpFNT/Py5tVwF/nrkBfvzREIhF+sLKGJFuCubOmYeKU6VBXV8fpv46jUWNnlC5tIHTc74IqzXVQFrl/PvPy8kJERAQOHjwILa2ccv+4cePw8uVLLFq06KueIzk5GQcPHkRkZNE8I8rHkpOScPrUSZm2qpbVkJmZiTdv3wiUqvAV1+3+IDb2OcJu3YSftxca1a+LRvXrIi42FosXzIPL6BFCx1M4I2NjaGlpSb9gAoCFRRXEx8UKmEq5ElLTERH333j6zCwJnr5IQ3kDHcT9O+zJ+N9hUDn/zvkSlvAqHUUN93eON2/eYOzIXxDx8CHWbdwMc3MLoSMVOhMTUyQlJcm0JSUlweijIVFF2dIlC7F96yYs8vRCqzbtpJ/ny3y80NixLho75nyeL1k4D64q8nleurSBzBdliypVkZGRgVepqQCAK5cvolmLVkLFIxUgd6XixIkTWLVqFWrUqCFtq1GjBhYuXIhhw77u16t169bh999/h7e3Nxo2bAgXFxdo/vur9p9//ompU6cWqeFPz549hdsEVxw/dQ6mpqYAgLt376BM2bIoU6aswOkKT3Hd7g9MTExx+NhxmbYRQwej34BB6NhJ9U6pa2trh4yMDDx+FA1ziyoAgOioKJkvnaru5qOXsKn4XzW2hLoIlQ1L4umLd4hPTcfTF+9Q39IQT188BQDUr1oWT1+8K3LzKQDubyBnvP3kiePw7OlTbNi8DVWqFs0zecnL1s4OmwLXIT09XVqduHUzFHXsHQROphgBa/yxf28QPL180bptewA5n+eHfpf9PB85dDD6qsjn+ZXLFzHHfQqO/nkG2v9W2h78cx+lDQxQpmxZpLx8iWdPY2BXp67ASb8fLFTIT+5Kxdu3b/Mt/WZnZyMr6+tL/J06dcLhw4eRmJiILl264PLly/JG+W7UrFUbNjY1MXf2TERGRODC+XNY5uONESNHCx2tUBXX7f5AQ0MDlSuby9zU1dVRtmxZmPzbyVIlFlWqwrlpc8yZ5Y5/7t/H5UsXsDFwHX78qficy3zDmSh0sCuPQU0sYGGsi0V9bJHxPhsn78QDALZdeISZ3WzQoLohGlQ3hHs3G2w8GyVw6m/D/Q0cOrAP14OvwWP+Qujp6yEpKRFJSYlITU0ROlqhcqjnCNNy5TF3tjsiIh5i44Z1uHM7HD169RY6WoFFRUViQ8AaDBk2AnXqOkj3aUrKy7yf5xqq83lua2cPbS1tLJo/B48fRePyxfNYucwbg34eDgCIjHiYU5nMdWICInnJXalo2bIlli1bhqVLl0rbYmJisGjRIjRr1kyu5ypdujSWLFmCK1euYN68eahVq1aRvFqruro6fvNfDc/FCzF4wE/Q0dFB/4GD0H/gYKGjFariut3F2ZKlPli6ZCGGDu4HbW0d9O03AP0GDBI6ltLcevwSYzdeh3s3G3j0qoXwJykYtOqKdM7E2pMPYaSnifUjHJGVJcHuK0+w/nTRHeZZ3Pf3qb9OIDs7G+NdZH8ocahXHxs2bxMoVeFTV1fHbytXY77HLPTv0xOVKpvDb/mqIn/hOwA4d/oUsrKysGHdGmxYt0Zm2Y3b9wVKVfh0dXWxfPV6LPP2xM/9e6Okri569PoJg4bkdCpevEhCKT09ziPIhe+F/EQSOb/Fv379GjNnzsSpU6eQnZ0NfX19vH79Gk2aNIG3tzcMDAy+KYhYLMbKlStx7NgxbN++HeXLl/+m5wGA9Pff/FAqgrKLYEdUEUQonh94P0xSvTOzfI0Hy7oJHUEQEvDvuzgprp/n77OK53aX1vl+z4w3eGe40l5ra3/br143NjYW8+bNQ0hICAwMDDB48GAMGTIEAHD37l3MnTsXDx48QLVq1TB//nzUqvXfRQyPHj2K3377DYmJiWjSpAkWLlyIsmUVN1xd7k7FBzExMYiMjMT79+9RpUoVWFpaKixUQbFTUbwU1/+EiuuXDnYqihd2KoqX4vp5zk7F92fILuV1Kjb3+/pOxU8//QQzMzNMmDABERERmDJlCry9vdG4cWO0bdsWXbp0Qe/evbFr1y788ccf+Ouvv1CyZEmEh4dj0KBBmD9/PqysrLB48WKULFkSAQEBCtuOb9qbEokEjx8/xuPHj5GQkJDnLBFERERERKQ4qampuHXrFsaMGQMLCwu0bt0azs7OuHLlCo4dOwYtLS1MmzYNlpaWmDVrFnR1dfHnn38CALZv344OHTqge/fusLKygpeXF86dO4eYmBiF5ZO7U/HPP/+gbdu2GD9+PA4fPoy9e/di9OjR6NGjB54+faqwYEREREREQhCJREq7fS1tbW3o6OjgwIEDyMzMRFRUFG7cuAFra2uEhYXBwcFB+nwikQh169bFrVu3AABhYWGoV6+e9LnKly8PMzMzhIWFKew9k7tTMXfuXNjZ2eHChQs4cOAADh48iHPnzqFChQqYM2eOwoIREREREak6sViMN2/eyNzE+VzBXktLCx4eHggKCoKdnR06dOiApk2b4scff0RiYiJMTGSvJWNoaIi4uDgAQEJCwmeXK4LcnYq7d+/CxcVFesVrANDX18ekSZNw48YNhQUjIiIiIhKCSIm3gIAAODg4yNw+NdchMjISLVq0QFBQEDw9PfHnn3/iyJEjSEtLk17z7QNNTU1p5yQ9Pf2zyxVB7lPK2tnZ4cqVK6hSpYpM+4fyCxERERERfZ1Ro0Zh6NChMm0fdwAA4MqVK9i3bx/OnTsHbW1t1K5dG/Hx8VizZg0qVaqUp4MgFoulF7DU0tLKd3l+1577Vl/VqfD395f+29zcHEuWLEFwcDBsbW2hpqaGBw8e4OjRoxg4cKDCghERERERCUFNidep0NTUzLcT8bE7d+7A3Nxc2lEAABsbG6xduxb16tXLc+KkpKQk6ZAnU1PTfJcbGxsrYAtyfFWn4tq1azL37e3tkZycjDNnzkjb7OzscOfOHYUFIyIiIiKiHCYmJnj8+DHEYrG0ExIVFYWKFSvCzs4O69evh0QigUgkgkQiwY0bNzB6dM7FO+3s7BAaGoqePXsCyLneRWxsLOzs7BSW76s6Fdu2qe6VQ4mIiIiIvnctW7aEt7c3Zs+ejTFjxiA6Ohpr167FpEmT0L59e/j6+mLx4sXo27cvdu/ejbS0NHTo0AEA0K9fPwwaNAh16tRB7dq1sXjxYjRv3hyVKlVSWD6551QAwL179/Dw4UNkZ2cDyLluhVgsxt27dzF//nyFhSMiIiIiUjYljn76anp6eti8eTMWL16M3r17o2zZshgzZgx++ukniEQiBAQEYO7cudizZw9q1KiBdevWoWTJkgByRhktWLAAK1asQGpqKho3boyFCxcqNJ/cnQp/f3/4+/vDyMgIycnJ0jFaWVlZaNOmjULDERERERFRjmrVqmHTpk35LrO1tcXBgwc/+diePXtKhz8VBrlPKRsUFIT58+fj4sWLKF++PLZt24bLly+jUaNGqFy5cmFkJCIiIiJSmu/x4nffO7k7FS9fvoSzszMAwNraGjdv3pRep+LYsWMKD0hERERERN83uTsVpqamiImJAQBYWlri7t27AIBSpUrhxYsXik1HRERERKRkIpHybqpC7jkVP/74I9zc3LBkyRK0bt0aQ4YMgYmJCS5fvgwrK6vCyEhERERERN8xuTsVo0ePRrly5aCjowNbW1u4u7tj9+7dMDAwwJIlSwojIxERERGR0ijz4neq4ptOKdu9e3fpv3/88Uf8+OOPSE9PR2JioqJyERERERFRESH3nIpPCQkJQdu2bRX1dEREREREguCcCvkprFNBRERERETF0zcNfyIiIiIiUlWqdP0IZWGlgoiIiIiICuSrKhUhISFfXOeff/4pcBhFkUiETiAMCYrphhdTGe+zhI4giH/8ugodQRCNPU8LHUEQF2a0EDqCIIrrj6Ti99lCRxCEdgl1oSPQR/iru/y+qlMxaNCgr3oyloqIiIiIiIqfr+pU3L9/v7BzEBERERF9F/hDufxY3SEiIiIiogLh2Z+IiIiIiHJRY6FCbqxUEBERERFRgbBTQUREREREBfJNw5+ysrJw4cIFPHr0CD179kR0dDSqVq0KPT09RecjIiIiIlIqDn+Sn9yditjYWAwfPhwpKSlITU1Fq1atsGHDBty8eROBgYGoUaNGYeQkIiIiIqLvlNzDnxYsWAAHBwdcuHABmpqaAAA/Pz80atQIixYtUnhAIiIiIiJlEolESrupCrk7FdevX8ewYcOgrv7f1R9LlCiBsWPH4s6dOwoNR0RERERE3z+5OxXa2tpITk7O0x4dHY1SpUopJBQRERERkVDURMq7qQq5OxV9+/aFh4cHzp49CyCnM7F//37MmTMHvXv3VnQ+IiIiIiL6zsk9UdvFxQX6+vqYN28e0tLSMHLkSBgaGmLIkCEYPnx4YWQkIiIiIlIaFZrqoDTfdErZQYMGYdCgQXj37h2ysrJ4KlkiIiIiomJM7k7FoUOHPru8e/fu3xiFiIiIiEh4aixVyE3uTsWKFStk7mdlZSE5ORkaGhqwtbVlp4KIiIiIqJiRu1Nx+vTpPG1v376Fh4cHL3xHREREREWe3GcyIsW8Z7q6uhg3bhw2bdqkiKcjIiIiIqIi5Jsmaufn/v37yM7OVtTTEREREREJglMq5Cd3p2LQoEF5Lin+9u1b/PPPPxgyZIiichERERERUREh9/AnJycnODo6ytzatWuH9evXY8qUKYWR8bsmFovRq3tnhARfk7Y9exqDUb8MQYP6ddCza0dcvnRRwISFQywWo3f3Lriea7svX7qAPj27oYGDHfr07IaLF84LmLBw5LfdXp6LYV/LSua2e+d2AVMqztnTJ+FUx0bmNmPKRADA1cuXMKBPDzRv6ADXUcPw+FG0sGEVKCE+HlPcxqNZYye0bdUUPl6eyMjIkFnnyZPHaFDPTqCEBVNCXYQ9ox3hYG4gbTMz0MaagXVwaUYz7BvjhAZVy8o8ZkCDSvh9QiNccm+GVQPsUKmsTr7PPbhhZRwd37Aw4ytcQnw8prqNR/PGTmjXqil8c+3vu3/fwc8DfkJjx7oYPOAnhIfdEjZsIRKLxViyaD6cG9VHy6aNsOI3P0gkEqFjKdTRIwfRwN4mz61h3ZoAgKkTXfIsu3j+rLChC0F+312AnM81JwdbgVJ9X9REIqXdVIXclYqUlBQMHjwYlStXLow8RUpGRgbcp01GZMRDaZtEIsGk8S6oVv0H7Ny9H2dOn4TbRFccPHIM5cubCZhWcTIyMjBz2hSZ7X7y5DEmTxgHl/ET0bxFq5ztHu+CQ0f/gFmFigKmVZz8thsAoiIjMW6iG7p27yFt09Utpex4hSI6MgLOzVrAfc48aZumphaiIh7CbfwY/DxsBNp37IwjB/fDZcRQ7Dn8O0qW1BUusAJIJBJMcRsPff3S2LhlO1JTUzHPYxbU1dUxafI0AEBcXCwmuIzO09EoCjTV1bCkpw2qmcgeo359aiMi4S0GbghB8xrG8O1TG71WX0Xcqwx0qGWKkU0tMPPAXTx58Q6jmlXBb31t0Wu17BeSCgbaGNWsCl6+EytzkwpEIpFg6r/7O/Df/T3fYxbU1NXx85DhGD1iKNq0bY95Cz1x6eJ5jB05DHsPHVWZz/PcvDwXITj4GlYHBOLd27eYMXUSzMzM0LtPX6GjKUzrth3QsFET6f3379/DZeRQNGnaHAAQHRWJeYuXor5jA+k6evqllR2zUOX33QUA4mJjMd5lVJH8XKPvg9yViiNHjkBNjXPiIyMjMKh/HzyNeSLTHhJ8FTExMZgzdwGqWlpi+IhRsLOrg0MH9guUVLEiIyMwuP9PiPlouxPi4tCzdx8MHDwEFStVwqCfh0JHpyTu3L4tUFLF+tR2A0B0dCSsrW1gZGQsveno5P8rblHzKDoKVS2rwdDIWHrT09fH/r1BsLWrg1Fjx8HcogpcJ06Grl4p/HnsqNCRC+xRdDRuh4dh/sIlsKxWHXUd6mGMyzj88e+2nTl1Ev1/6oUSmpoCJ5VfFaOS2DLcARXLyB6f9S3KoGJZHSz6/T6ik95h06XHCH+aim72OV+cS2lrYPnJSFyKSEbMizRsufQYVYx0UaZkCZnnmdnJCvfjXittexThw/6e99H+/vPYURz93yEYlDbAzDnzUKVqVQwcPAR17B2wL2iX0LEVLjU1BYcO7ofHvIWoXdsWTg0aYtDPw3A7PEzoaAqlra0t83n25+//AyDB2PFuEIvFiH3+DDY1a8uso1kE/9Y/5VPfXU6fOol+P/WEZgnV2daCEomUd1MVcvcOhgwZgvnz5+PSpUuIjo7G8+fPZW7FRWhIMOo7OmHLjiCZ9vCwMFjb2ECnZElpWx17B5UpmYeGhPy73btl2us5OmHqjJkAgMzMTBzcvw/iTDFq1a4tREyF+9R2v3nzBgnx8TC3sBAmWCGLjopEZXOLPO3PnsagZq3/SuQikQjVqv2AO2FF/wuIkZERVq1dD0MjI5n2N6/fAAAuXDiHsS7jMe3f470ocTAvg+uPXmLIxlCZ9toV9XE/9g3SM/872catmFTYVtQHAOy9/gwHbuR8vpfSUkef+hURkfAGL99lStfvZFsO2iXUcPhmrBK2RHGMjIzg/4n9/ezpU1jb1IS6urq0vfoPNVTm8zy3mzdCUapUKdSr7yhtG/bLSMxf5ClgqsKVmpqCbZsDMXacGzQ1NXOGcIpEKlNdz8+nvrtcOH8WLq4TMHXGLIGSkSr45ovfXbhwAQCkk7YlEglEIhHu3bv3zWEkEglSUlJQpkyZb34OZenTt3++7UlJiTA2NpFpMzQ0RHx8nDJiFbo+fft9dvmTJ4/Rs0tHZGVlYfykySrz4fyp7Y6OioRIJMKGdQG4dPE8Spc2wMCfh6Brtx75rl+USCQSPH70CFevXMLmwHXIzs5GqzbtMHKsK8oaGiIxIV5m/fi4OOiXLvrDBPT09dGosbP0fnZ2NoJ27YCjU85wCI95CwEA10Ou5fv479m+0Gf5thuV0kTia9khD8lvxDDR05Jp61anPOZ2tUbG+yy47PivA2lQsgTGt7LEmO23UNNMT/HBC9Hn9ndZQ0M8+Oe+zPrxcbFISUlRcsrC9/RpDMzMKuB/hw8hcMNaZGZmolv3nvhl5BiVHZ1wYO9uGBkbo2WbdgByKrOlSpXC/NkzcCM0GKam5fHLaBc0atJU4KSK86nvLnPnLwKAPHMsijM1FaogKMtXdSpCQkJgb28PDQ0NnDp1qsAvOmHCBCxevBilSuWM6c3MzIS3tzf27NmDjIwMGBgYYMSIERg2bFiBX0vZ0tPS8gyL0NTURKa46IwxLogyZcpi++69CA+7BV+vX1GpcmW0/vcDWxVFR0dBJBLBokoV9O0/EKHXg7FongdK6ZZCy9ZthI5XIHGxz5GengbNEppY4uWH58+fwXfpEqSnp6NNuw6YMsEFbTt0QoNGTXD82FHcvXsHDvUcv/zERcxvft64f+8utu/aK3SUQqNdQh3iLNlTgmdmZUNTQ/bL5LWoF+i3Lhjd6pTHsp9qo9+6EDxPSceUttXxv7BYRCW+LXKdio8t/3d/b/t3f28IWIMD+/aga/eeCL52BWfPnoaJickXnqXoSXv3Dk+ePMa+vbsxf6EnkhITsWiBB7S1dTB4SNH7v/hLJBIJjhzcj4E/D5e2PX4UhfT0dDg1aozBQ3/B2TMnMXWiCzZs2QXrmrUETEtUNHxVp2Lw4MG4ePEiDA0NUaFChQK/6IkTJ+Dh4SHtVKxYsQInTpyAl5cXLC0tcffuXXh7eyM9PR1jx44t8Ospk6aWFtI++hVLLBZDW1tbmEBKpqenBytrG1hZ2yAqMgK7d2xX6U5Fl67d0ax5C5QubQAA+KFGDTx+/Ah7g3YV+U5FebMKOHHuMvT1S0MkEuEHK2tkZ2dj3qzpmDhlOn4ZNRYzJk9AVlYWHOo7omPnrnjz5o3QsRVquZ8Pdm7fil+9/VCt+g9Cxyk04vfZKK0jOz+ihLqazHAoAIh7lYG4Vxnw+vMhHMzLoItdeYQ/TUXtiqWxcK3sL/pFUX77e/bcBfD+dTGWLJyHH2pY4cef+smc/U1VqKtr4M2bN/D08oWZWc7/87Fxz7Fn9y6V7FTcu3sHCQnxaNO+g7Rt2Igx6NNvIPT/nZhdvYYV7t+7i0MH9rJTQfQVvqpToehTyn38fH/++Sdmz56N1q1bAwAsLS2hr6+POXPmFLlOhYmJKSIjImTakpKSYGSser9s5RYZ8RCpqamo61BP2lbVshquh4QImKrwiUQiaYfig6pVLRFyTTW+dHy8bRZVqiIjIwOvUlMxdMRoDPh5GN68eY2yZQ0xc+oklDdTnTPi/LpkIfbt2Y1Fnl4q3TEGgITXGahqLHvWLqNSmkh6kzMkqp6FARJfi/E4+Z10eXTSWxjolEC7mqYoV1oLp6bknFFHXU2EEupquDijKcbtDMPNJ6nK25ACWJprf7fKtb+79eiFzl2748WLZBgbm+A3P2+YKeDHte+NkbExtLS0pB0KALCwqIL4uKI1R+ZrXb10EfZ1HaQdCABQU1OTuQ/kfOZFR0Z8/HAqBlTpVK/K8tUDJT++4F1BiEQimedTU1NDxYqyY+8rV66Mt2/fKuw1lcXWzg737/2N9PR0adutm6GobVs0z2f/tc6dPYOF8+bIdBjv/v03qlStKmCqwrfafwVG/TJUpu2f+/dgUaWKQIkU5+rli2jTrCHS09KkbQ//uY/SBgYIvnYFfl6e0NTURNmyhkhPT0fo9WA41HMSMLHiBKzxx/69QfD08kX7Dp2EjlPobj99BavyetDKNdypTuXSuP30FQBgSCNzDGxQSbpMTQTUKKeH6KS3WH4yAr1XX0O/gBD0CwjB2rPRSHydgX4BIbj7vGicCSr3/m6Xa3+HBF/FjKluUFdXh7GxCSQSCS5fOI969VXjOM/N1tYOGRkZMtebiY6KkulkqJK/74TD1q6uTNsCj5lYNE92ovLDf+7D3KLof54TKcNXT9Tu1avXV03W+po5FxKJBLNnz0b16tVRpUoV1KpVC1u3bsWSJUsA5JxDedWqVahTp87XxvtuONRzhGm58pg72x0jRo/F+bNncOd2uEqfQQMAOnXuik0b1mHFMl9079UbVy9fwrGjR/KcLUnVNGvWAps2rMPWTYFo0aoNrly+hKNHDmPdxi1CRyuw2nb20NLSxuL5Hvhl9Fg8exqDlct8MOjn4ahsboFFc2fB3qEeLKtVh/9vvjA1LYdGTZy//MTfuaioSKwPWIOhw0fCvq4DkpISpcuMjIwFTFZ4Qh+/RHxqOuZ1tcb6C4/Q9Acj1DTTx7zDOSfe2HP9Gbx610Lo4xTci32NQQ0qQUtDDf8Li0NaZpbMWaBevBUjK1uCmJdpn3q570pUVCQ2/Lu/63y0v83Nq+D8uTPYG7QLDRs1wbYtG/Hq1St06dZduMCFxKJKVTg3bY45s9wxa848JCcnYmPgOowYOUboaIUiMuIh2nfsItPm3KwF5syYjLoOjqhtVwcn/vgdYbduYMac+QKlJCGxUCG/r+5UDB06FHp6ipmA5+/vj4iICERGRuLChQuIjo5Geno6ZsyYAX19fTRt2hQ6OjoIDAxUyOspk7q6On5buRrzPWahf5+eqFTZHH7LV6nkhZJyMy1XDqsCNsBnqSd279yO8mYV4OW3HNY2NYWOVqhq1q4NL7/lWOO/Aqv9V8DMrAKWLPWBXR17oaMVmK6uLlasXodl3r9iSP8fUVJXFz169cHAIcMgEokwbaYHlvt6ITU1BfUdG8Bv5VqVOEvM2dOnkJWVhQ3r1mDDujUyy27eLvrzBvKTLQHcgm7Do6sVdoyoh5gXaZi85zbiXuUMfzr/IAmex/7BqGZVYKqvhdtPX8Flxy2kZWYJnLzgzn1mf9+4fR9LfZZhmY8Xlvl6obatHdas31TkL/D4KUuW+mDpkoUYOrgftLV10LffAPQbMEjoWIXi5Ytk6Onry7S1aNUGU909sGnDWsTHxaKKZTX85r9OZas1RIomknzFhAlra2vpRO3C8vz5c5j9Ox774sWLsLe3h67ut31wp2V+eR1VJIFi577Q9038PvvLK6kgTfWi33H5Fk1+PSN0BEFcmNFC6AiCKK7judNVoKP6LbRLqH95JRX00fkhviuLTylvLs2sVtWU9lqFSZCJ2vkxyzXBs0mTJoX+ekREREREpBhf1ano0aMHtLS0vrwiEREREVERJ0LxrBYWxFd1Kjw9VXuSMRERERERfbuvnqhNRERERFQcqLFQIbfiOeORiIiIiIgUhpUKIiIiIqJcWKmQHysVRERERERUIKxUEBERERHlIiqm14opCFYqiIiIiIioQFipICIiIiLKhXMq5MdKBRERERERFQgrFUREREREuXBKhfxYqSAiIiIiogJhp4KIiIiIiAqEw5+IiIiIiHJR4/gnubFSQUREREREBcJKBRERERFRLjylrPxYqSAiIiIiKgLEYjHmz5+P+vXro1GjRvDz84NEIgEA3L17Fz/++CPs7OzQq1cv3LlzR+axR48eRevWrWFnZwcXFxe8ePFCodnYqSAiIiIiykUkUt5NHosWLcLly5cRGBgIX19f7NmzB0FBQXj37h1GjhyJevXq4cCBA7C3t8eoUaPw7t07AEB4eDhmzZoFV1dXBAUF4dWrV3B3d1foe8bhT0RERERE37mUlBTs378fmzZtgq2tLQBg2LBhCAsLg4aGBrS0tDBt2jSIRCLMmjUL58+fx59//omePXti+/bt6NChA7p37w4A8PLyQosWLRATE4NKlSopJB8rFUREREREuahBpLTb1woNDUWpUqXg6OgobRs5ciQ8PT0RFhYGBwcHiP4tfYhEItStWxe3bt0CAISFhaFevXrSx5UvXx5mZmYICwtTzBsGFa1UFNezgEmyhU4gjPfZEqEjCEK7hLrQEQQhKZ67G5fcWwodQRCmg7YKHUEQCdsHCx1BEFoliudvncX1ewvlEIvFEIvFMm2amprQ1NSUaYuJiUGFChVw6NAhrF27FpmZmejZsyfGjBmDxMREVKtWTWZ9Q0NDPHz4EACQkJAAExOTPMvj4uIUth0q2akgIiIiIvpWyuzoBQQEwN/fX6bN1dUV48aNk2l79+4dHj9+jN27d8PT0xOJiYnw8PCAjo4O0tLS8nRCNDU1pZ2V9PT0zy5XBHYqiIiIiIgEMmrUKAwdOlSm7eMOAABoaGjgzZs38PX1RYUKFQAAz58/x65du2Bubp6ngyAWi6GtrQ0A0NLSyne5jo6OwraDnQoiIiIiolyUeZ2K/IY65cfY2BhaWlrSDgUAVKlSBbGxsXB0dERSUpLM+klJSdIhT6ampvkuNzY2VsAW5CiegxeJiIiIiIoQOzs7ZGRkIDo6WtoWFRWFChUqwM7ODjdv3pRes0IikeDGjRuws7OTPjY0NFT6uNjYWMTGxkqXKwI7FUREREREuaiJREq7fa2qVauiefPmcHd3x/3793HhwgWsW7cO/fr1Q/v27fHq1SssXrwYERERWLx4MdLS0tChQwcAQL9+/XD48GHs3bsX9+/fx7Rp09C8eXOFnU4WYKeCiIiIiKhI8PHxQeXKldGvXz9Mnz4dAwYMwKBBg1CqVCkEBAQgNDQUPXv2RFhYGNatW4eSJUsCAOzt7bFgwQKsWrUK/fr1Q+nSpeHp6anQbCKJRPVO0Jj+XugEwsgupqdWLa6nlNXUKJ6/CajeJxZ9Dk8pW7xkF9M/cHl+rVYl2t/xzN711x4r7bVGOJkr7bUKU/H8VkJERERERArzHfcRiYiIiIiUr7hWjwqClQoiIiIiIioQViqIiIiIiHJhoUJ+rFQQEREREVGBsFNBREREREQFwuFPRERERES58Fd3+fE9IyIiIiKiAmGlgoiIiIgoFxFnasuNlQoiIiIiIioQViqIiIiIiHJhnUJ+rFQQEREREVGBsFJBRERERJSLGudUyI2VikLgOmYk5sycIXSMQpEQH48pbuPRrLET2rZqCh8vT2RkZAAAnj19ilG/DEVDR3v07NYJVy5fFDit4pw59Rfq21nL3KZPngAAuHj+LPr36YGmDRzQr3c3nDt7WuC0hefJ48cYPWI4GtSzR7tWzbF54wahIxUqsViMXt07IyT4mrTt2dMYjPplCBrUr4OeXTvi8iXVOc5zy2/bAeDJk8dwcrAVKFXBaGqo4ap3FzSxMZW21a9mhL8WtMfzzf0Q6tcNg1tUky67vbInXu0enOc2vactmtiY5rvs1e7BqGioK8TmFVh8fDwmTxwP54aOaN3CGd5L//t8V0VisRi9u3fB9Y+OcQB4/fo12rZsiiOHDgiQTDmK2+c5FT5WKhTsj2O/48L5c+jarYfQURROIpFgitt46OuXxsYt25Gamop5HrOgrq6OiW5TMWmCC6pX/wE7du/DmdMn4TZxHA4c/h3ly5sJHb3AoqMi4dysBWZ6zJe2aWlq4eGDfzDNbTzGT5qKxs5NceXyRcyYPBFbdu7BDzWsBEyseNnZ2XAdOxI1a9VG0P6DePL4MWZMdYOJiSk6du4idDyFy8jIgPu0yYiMeChtk0gkmDTeBdWq/4Cdu/f/e5y74uCRYypxnH+Q37YDQFxsLMa7jCqSXzS1SqghcJwzbCqVkbaZlNbGvhmtEHjyAUavvoQ6VQyxekwjxKek4fjNZ2g+83eoq/33a2X3BuaY3cceO89HIu5lGqqN2iPzGlsmNsOL1xl4mvxWadulKBKJBFMmjYe+vj42bduBV6mpmDt7JtTV1eA2ZbrQ8RQuIyMDM6dNyXOMf7DczweJCQlKTqU8xe3z/FuwTiE/dioUKDUlBct8vVCzVm2hoxSKR9HRuB0ehpNnLsLQyAgAMMZlHJb5eqFxk6Z4GhODLdt2QadkSVStaongq1dx+OB+jB47TuDkBRcdFQXLatVhZGQs075543rUc2yAvgMGAQAqVTbHhbNncPLEnyrXqUhOTkINK2vM9pgHXd1SMDe3gGODhrh5I1Tl/hOKjIyA+7TJgEQi0x4SfBUxMTHYsn13znFuaYnga1dw6MB+jHEp+sc58OltP33qJBbOnwPjj/4GioIaFUojcJwzPh7N0Ll+ZSSkpmHB7psAgMi413CuWQ4/Nq6C4zefIfn1f50nfZ0SmN7TFrO2X0dMUk6nISE1Xbq8dyML2FQygP2kQ4W+PYXhUXQUwsNu4fS5S9LP97Gu4+Hrs1TlOhWRkRGYOW0KJB8d4x/cvBGK4GtX83zeq5Li9HlOysPhTwrk67MUnbt0g6VltS+vXAQZGRlh1dr10v9wPnjz+g1uh9+ClbUNdEqWlLbb162L8LBbSk5ZOKKjIlDZ3CJPe+eu3eE6wS1P+5vXr5WQSrmMjU3g7fsbdHVLQSKR4OaNUNy4HoJ6jo5CR1O40JBg1Hd0wpYdQTLt4WFhsLaRPc7r2DuozHEOfHrbL5w/CxfXCZg6Y5ZAyb5dExtTXLgbh9Zz/pBpPxn2DGPXXM6zvn5JzTxt47vURFxKGrafjcizTENdhDk/2cPn0G28eF30qjgAYGhkjNUBG/L9fFc1oSEh/x7ju/MsE4vFWDh3DtxnzUEJzRICpFOO4vR5/q1EIuXdVAUrFQpy7eoV3Lh+HfsO/Q+LF8wTOk6h0NPXR6PGztL72dnZCNq1A45ODZCYmAhjExOZ9csaGiE+Pl7ZMRVOIpHg8aNHuHr5IjZvCEBWdjZatWmH0S7jUKWqpcy6kREPERJ8FT1//EmgtMrRoU1LxMY+R9NmLdC6TTuh4yhcn779821PSkqEsbHscW5oaIj4+DhlxFKKT2373PmLACDPHIuiIPCvB/m2P0l8iyeJ/w1VMtLXRq9GFvh1X5jMejqa6hjZzgoTN1z9uIADAOjZwAKlS2pi/fF/FJpbmfT19dG4iezn++6d2+HUoIGAqQpHn779PrkscN1a1LC2RsPGTZSYSFiq/nlOyiNYpWLPnj2YNSvnFy+JRILNmzejffv2qFOnDjp16oQdO3YIFU1uGRkZWDR/Ltxne0BbW1voOErzm5837t+7C9fxE5Geng7Nj37V0dTURKZYLFA6xYmLfY709DSU0NTEEu9lmOA2FX8eO4rlfj4y66W8fInpkyfAto49mrVoJVBa5fD9bQVWrFqLf/65B++lnkLHUZr0tJzjIDdVOc6LO+0S6tju1gwJKWnYeFK2E9KzoQXepmfi8LXH+T52SKvq2HLmIdIzs5QRVSmW+Xrj3r27cJ0wSegoShMZGYF9e4IwZZq70FGUqrh+nn+JSCRS2k1VCFKpWLZsGfbs2YNh/2/vvsOiuNoogB86SO8RVOxKEym22LH3EmNQg12xYteADYOKgr2BNfaO3VhjYtRPiaKA2CmCREVAQRFY2n5/EDesJYYsuyPL+eWZ58nemWXPsDi7d957Z4YOBQAEBwdjx44dGDVqFKpVq4bY2FisXbsWr1+/xujRo4WIWCIh69bAzt5B6iyPslu5bAl279yORUHLULNWbWhpaiE9I1tqm9zcXKXoZFW0ssb536/CwMAQKioqqFPXFmKxGHN8p2PS1BlQU1NDWloqxnkNg7iwEIuXrISqqnKPLHw3byhXJILPjKmYMnX6B1+2lZGmlhay09Ol2pTl77w809VSx55prVHzKwN08DuN7FzpzkHPRjY4dPUxCgo/LFOYGWjj67qWmPrTH4qKK3fLlwZh145tCFyyHLVq1RY6jkKIxWL4z52N0ePGfzAETNmV1+M5lT5BOhWhoaFYvnw5Gv9VVj106BD8/f3Rtm1bAECLFi1Qs2ZN+Pj4lIlOxelTJ5GWmorGbs4AgLy8orOW586ewbUbt4SMJheLFvrj4P69mB8QKCmVWlhaIDZW+ioaaakpMDNXjoluhoZGUo+rVqsOkUiE1xkZyMvLw+gRgwEAIZu3w9jERPEBFSAtNRWRkRFwb9NW0la9Rk3k5eUh820mjDWVc7+Ls7CwRGyM9Jj61NRUmL03JIrKDn0dDYT+0AbVLfXRdf5ZxD6Xng+lqa6KZnaWWHYs+qPPb+tkhYSUTNx9kq6AtPIXsMAfB/btwYJFQWjbvvwMhXn27CkiI27h4YMHWBYUCADIycnGgh/9cOb0KawN2ShswFLG4/nnKfepQfkQpFORm5sLPT09yWMNDQ2Yv/fl09zcHNnZ2e8/9Yu0eesO5OflSx6v+GtYzMTJU4WKJDfrg9cg9MA+BAQuRbv2HSXtjvXq46fNG5GTkyM5axtx6ybqO7sIFbXUXL1yGbN9puLEmV+hraMDAHj44D4MjYygra2N0SMGQ1VVFcGbtir11UL+/DMJkyeMw5lfLsLSsug6/3fvRsPYxATGxuXjA6iekxN+2rzhvb/zcNR3dhU4Gf0XKirAzsmtUNVCD51+PINHT19/sI19FWNoqKsiPCb1oz/DraYZrj1QjkuPhqxbg4P792Jx0DK069Dx809QIhYWljj68xmpthFDBqLfAE907qJ8V0Pi8ZzkQZCOWJcuXTB16lTcuHEDAODl5YXFixfj+fOiyY4JCQmYN28e2rVrJ0S8ErOyskYVGxvJoqurC11dXVSxsRE6WqmKi4vFxvXBGDx0BJxdXJGamiJZXN0awPKripg72xexMY+wZdMGRN+OQs/efYSOLbN69Z2hpaWN+fNm4/HjeFy5/DtWLQvCwMHD8NPmDUhKegI//6JxqO9+H8p49Sd7B0fY2dlj7ixfxMbE4NLvF7F8SRBGjBwldDSFcXVrWPR3PssHMcX+znt9U/b/zsujga1roYW9JcZvuIqMt7mwMNSGhaE2jHX/HvphW9kIj5MzkZtf+NGfYVvZCPeTMhQVWW7iYmOxIWQdhgz76/iekiJZygN1dXVUqWIjtaipqcHExAQWlpaf/wFlDI/nn8c5FSUnSKXCx8cH8+fPx+DBg6Gvrw9ra2s8fvwYrVu3hpaWFkQiEVq2bIlZs2YJEY8+4bcLv6CgoACbNgRj04ZgqXW3bt/H8lVrMW/OTPT/7htUrmKDpSvWKMUNwXR1dbEqeCOWBQVgUL8+qKCri959voPn4GH4tmcXiHJyMPh76as9deneU9LRUBZqampYsWYdAhb4Y+CA76Cjo4P+33ui//cDhY6mMGpqalixel3R33nf3qhcxQbLVq5Vir/z8qhHwypQU1XFgRnSF1a4dPc5uvx4FkDRDfLS3356In7R+rJ5Gdnifv3r+L5xfTA2rpc+vkfeKbtXtaKP4/Gc5EFF/Km7vyhARkYGwsPD8eTJE2RlZUFNTQ0WFhZwcnJCtWrV/vPPzcn//DbKqPAjkwjLg/xyut+a6uVzxKdwRywSgqXndqEjCOLFzvL55a6wnP4DV1Wis9Ulof0F39hgf8RThb1W3/rKcWJK0LfT0NAQ7u7uQkYgIiIiIpJSPrt5simfpzqJiIiIiKjUfMGFJyIiIiIixVOmCdSKwkoFERERERHJhJUKIiIiIqJieNa95Pg7IyIiIiIimbBSQURERERUDOdUlBwrFUREREREJBNWKoiIiIiIimGdouRYqSAiIiIiIpmwUkFEREREVAynVJQcKxVERERERCQTViqIiIiIiIpR5ayKEmOlgoiIiIiIZMJKBRERERFRMZxTUXKsVBARERERkUxYqSAiIiIiKkaFcypKjJUKIiIiIiKSCSsVRERERETFcE5FybFSQUREREREMmGngoiIiIiIZKKUw5/EYqETCENVtXzW6tTL526X279zMcrpjpdTL3YOFDqCIIw7LhI6giBenvpB6AiCyCsoFDqCILTVv9xz27z5Xcl9ue8mERERERGVCUpZqSAiIiIi+q84UbvkWKkgIiIiIiKZsFJBRERERFQMKxUlx0oFERERERHJhJUKIiIiIqJiVHj1pxJjpYKIiIiIiGTCSgURERERUTHl9NZfMmGlgoiIiIiIZMJKBRERERFRMZxTUXKsVBARERERkUxYqSAiIiIiKob3qSg5ViqIiIiIiMqYkSNH4ocffpA8vnv3Lr799ls4OTnhm2++QXR0tNT2J06cQNu2beHk5ISxY8fi5cuXpZqHnQoiIiIiomJUFPjff3Hy5ElcvHhR8jgrKwsjR46Em5sbDh06BGdnZ3h5eSErKwsAEBUVhZkzZ2LcuHHYt28fXr9+DR8fn1L5Xb3DTgURERERURmRnp6OwMBAODo6Stp+/vlnaGlpYfr06ahRowZmzpwJXV1dnD59GgCwc+dOdOrUCT179kTdunURGBiIixcv4smTJ6WWi50KIiIiIqJiVFUUt5TU4sWL0aNHD9SsWVPSFhkZCVdXV6j8NRlERUUFLi4uiIiIkKx3c3OTbF+xYkVYWVkhMjJSpt9TcexUEBEREREJJDc3F5mZmVJLbm7uR7e9evUqbty4gTFjxki1p6SkwMLCQqrN1NQUz58/BwC8ePHiH9eXBnYqiIiIiIgEsn79eri6ukot69ev/2A7kUiEuXPnYs6cOdDW1pZal52dDU1NTak2TU1NSeckJyfnH9eXBl5SloiIiIioGEXe/M7LywtDhgyRanu/AwAAa9asgYODA5o3b/7BOi0trQ86CLm5uZLOx6fW6+joyBpfgp0KIiIiIiKBaGpqfrQT8b6TJ08iNTUVzs7OACDpJJw5cwZdu3ZFamqq1PapqamSIU+WlpYfXW9ubl4auwCAw59KzfNnzzB+jBeaNnJBp/bu2Lljq9CRFG7c6JGY7fvD5zcsw3Jzc9GnZzfc+CNM0hYYsADODnWllr27dwqYUn5yc3OxcP48NP+6AdxbfI1VK5ZBLBYLHUtuPvZ+/+/KJfTt3QONXZ3Qt3cPXL70u4AJ5eNj+/3s2VOMGz0STdzqo3un9jh7+pSACeVLJBJh7mxfNGvshjYtm2Hb1i1CR5KJpoYabmwchuZOVSRtbd2qIWz9ULw8OQVh64eifYPqUs/p19YekT+NQPLRSdjn1xuWxrof/dmT+jbE/Z2j5Zpf3srbcQ0AJoz1gt+sDy8nGnEzHD06tRMg0ZdHRUVxy7+1Y8cOHD9+HEeOHMGRI0fg7u4Od3d3HDlyBE5OTrh165bkb1csFuPmzZtwcnICADg5OSE8PFzys549e4Znz55J1pcGdipKyfSpE1GhQgXs3n8I03/wxZpVK3Dh/DmhYynMqZ9P4tLvFz+/YRkmEongM20KYmMeSbXHxcZi/MTJOPfbJcnSo9c3AqWUr8CA+bh29X9Yt34zAgKX4nDofoQe2Cd0LLn42PudmJiAKRPGo3vPXjh45AS69eiJyd5j8fTPJAGTlq6P7Xd+fj68x3hBXV0dew4cwsAhQzHzh+mIefRQwKTys2xJIO5GR2Pjlm3wnT0X69etwbkzp4WO9Z9oaahhu2932Ff7+2xkdSsj7PPrjZ1nb8Nl+CbsOnsb++f1RhVLQwBFHY4N07og+Eg4mo/bhszsXBwJ6PvBl5+qFQ0x07OZIndHLsrTcQ0Azpw6iSsfORkS8/AhZkyZiEJxoQCp6N+wtraGjY2NZNHV1YWuri5sbGzQsWNHvH79GgsWLEBMTAwWLFiA7OxsdOrUCQDQr18/HD16FAcOHMD9+/cxffp0tGrVCpUrVy61fOxUlILXGRmIiozACK/RsLGpitbubdG0aXOEhV0VOppCZKSnY/nSQNg7OH5+4zIqNjYGA/t/hydPEj9YFx8fC1tbO5iZmUuW0hyj+KXIyEjHkcOhmOPnD0fHemjUuAk8Bw3F7ajSuxzdl+JT7/eL58/Ru09ffD9wMCpVrgzPQUOgo1MB0bdvC5S0dH1qvy9f+h3Pnz/H/IBAVK1WHX36eqBZixaIjLglUFL5ycrKwuHQA5juMxO2dvZo07YdBg8djr17dgkdrcTqVjHFxdUDUc3KWKrd2twAW05GYHXodTx+loFVodfxNicPDepWBACM7umKvb/cQcjRm3j45CXGLj+NyhYGaONaTernrJ7QEZGxyQrbH3koT8c1oGh/Vy1bArv3Pq9DD+zD0IH9YGJqKlCyL4+KApfSoKenh/Xr1yM8PBy9e/dGZGQkNmzYgAoVKgAAnJ2d8eOPP2Lt2rXo168fDA0NERAQUEqvXoRzKkqBlrY2tHV0cPTIIXhPnII/k54g4tZNjPOeKHQ0hVi6ZDG6duuBlBcvhI4iN+HXr6NBw0YY6z0RXzdwlrRnZmbiRXIybKpWFS6cgty6GQ49PT24NWgoaRs6fKSAieTnU++3W8NGcGvYCACQl5eHE8eOIjcvFw6OytGh/tR+37gehoaNGkNPT0/StnzVWiEiyt3DB/eRn5+P+vX/3n9nF1ds2hCCwsJCqKqWnXNxzZ2q4PfIRMzdchEvT06VtF+KTMSlyKKOo7qaKga0c4CWhhqu338GAKhW0Qhn/oiVbJ+Tm4+4P1+hkZ01zt+IBwD0b+eACtrq2HoqqkxXK8rTcQ0AViwNQueu3ZGSIv15/b/Ll+A3PwBv377FhuA1AqWjklq0aJHU43r16uHw4cOf3L53797o3bu33PII0qmws7PDoEGDMHnyZGhoaAgRoVRpaWnBZ+YcLFrgj907t6OgoADde/ZGr2++FTqa3IVdu4qbN27g4JHjWPCjn9Bx5KavR7+PtsfHxUJFRQWbNqzHlcu/w9DQCN8PGozuPXopOKH8JSU9gZWVNY4fPYLNm0KQl5eHHj17Y/jI0WXqi9a/8an3+53ExAT07tYZBQUF8J40BVbWlRSUTL4+td9/JiXBysoaK5cvxcnjR2FkZIzRY8ejdZu2Ck4of6kpKTAyMoZGsUmTpqZmEIlESE9Ph4mJiYDpSmbj8X+uJFW3MkLkTyOhrqaKWRt/RWJyBgDgxau3sDLVl2ynogJYmenBzKCoAmtmqIP5w1uhy/S9cK1TUX47oADl6bh2PewaboXfwN7Qo1g0f57UuqUrizoSx49++gtpeaNakskOBECg4U+FhYW4cOECunbtinPnlGPeQXxcLFq2ao3tu/Zh3vwAnD97GidPHBM6llyJRCLMnzcXPrM+vF5yeREfHwcVFRVUrVYNq9dtQK9v+mC+3xylnE+TnZWFxMQEHDywF/P8AzB5ygzs2bUDO7dvFTqawhkbm2Dn3gPwmTUHIWtX4/y5M0JHkqusrCwcO3oYb15nYOWaYHTt3gPTJk/AnWjlGPZVXHbOx6/1DgB5pXg99y9BakY2mo3dhgmrzmDWoObo2bwOAODgb/cwopszGtlaQV1NFdP7fw0LY11oaKgBAAJHt8HOs7dxLyH1n358mVBejmsikQgL/edihu/scvt5TfInSKVCRUUF27Ztw9GjR+Hr64uVK1fC09MTnTt3hr6+/ud/wBcm7NpVHA49iDO/XIS2tjbsHRzxIjkZm9YHo0vX7kLHk5uQdWtgZ++Aps0+vF5yedGte0+0bNUahoZGAIDadeogIeExDuzbA/e2ynUFDTU1dWRmZiIgcCmsrKwBAM+eP8X+vXswcPBQgdMplr6+Pura2qGurR3iYmOwd9dOtG3XQehYcqOupgYjQyP4zvaDqqoqbO3scetmOA4d3K90c6k+dS13AEr3Zez1WxEiY5IRGZMM2ypmGN3TFUcuPcCWnyNhX80c51d8DwA4/Pt9nP4jDm/eitDWrRoa2VljzPDNAqcvHeXluLYxZC1s7RzQpGnZHaqmaKxTlJwgnQqxWAwNDQ14eXnBw8MDu3fvxoYNG+Dv748GDRrAxcUFNWrUgKGhIZo2bSpExBK5ezcaVWxspD5w6traYfPGEAFTyd/pUyeRlpqKxm5FY4/z8oo+eM+dPYNrN5RvAufHqKioSDoU71SvXgPXw8I+/oQyzMzcHFpaWpIPXgCoWrUakp8/EzCVYsXGPEJGRgZcXN0kbdVr1MSN69cFTCV/ZubmgIqK1HCQqlWr4eHDBwKmkg8LC0ukp79Cfn4+1NWLPiJTU1Ogra0NfQMDgdOVDlsbM5joa+NK9N9XLbuXmCq55GxhoRiTVp+D74Zfoa2pjldvcnBpzUBcCI/Ht61sUcncAE9CvQEUzcnQVFdDyvHJ6OmzX+pnlgXl5bh29tTPSEtLRfNGrgCA3L8+r385dxaXwsL/6alE/5pglYp3DA0NMXr0aIwePRpRUVG4fPkyoqKicPjwYbx8+RIRERFCRCwRC3MLPElMQF5eLjQ0isrkj+PjlGac9ads3roD+Xn5kscrli0BAEycPPVTT1E669asQmTELazf9JOk7cH9e6hardo/PKtsqlfPCSKRCAmP42FTtWj/4uPipD6Mld3F337F8aOHcejYz5Lj2N07d1CtevXPPLNsc6znhE3rQ1BQUAA1taIhMHFxsUr53tepawt1dXVERUZIOo+3bobD3sFRacbYd2lSE9+3d0T9oRslbc61vsKDxDQAwPhvGkBLQw1L9l5DtigfX5nowqmmJbyW/IzQi/exePf/JM/r2awOxvRyRfspu/E0NVPh+yKr8nJcW79lG/Lz//68XrV8KQDAe9IUoSJ9+ViqKDFBjpCfuqlMvXr1MGbMGISEhODChQtlokMBAC1auUNdXQPz5sxCwuN4XPztAjZvDEH/AZ5CR5MrKytrVLGxkSzvrpdcxcZG6GgK07Jla9y8cR3bf9qMJ4mJ2L93D04cO6pUZfN3qlarjuYtWmH2TB88uH8f/7tyCVs2b8C33/3zpGZl0qVrd6SmpGDV8qVISHiMfXt24ecTx5T6ajEA0LFzVxSKCxEwfx4SExOwf+9u/O/yJfTuo3wXo9DR0UG3Hj0x/0c/RN+OwoVfzmP71i3o//1AoaOVmj3n7+ArU13MH94KNayN4dXdBf3a2CNoT9Fl0B8/S8fk7xqjhVMV2NqYYfecXjgdFou7j1ORkp6FuKfpkuVFehbyC8SIe5qOnNz8z7zyl6e8HNcqWlmjchUbyfLu87pylfLzeU3yJ0ilIiAgoEzOnfgUfX19rN+8FYEBCzDAow+MjU0w3Gs0vvn2O6GjkZzZOzoicNlKBK9ZhXVrVsHKyhoLFy+BU7HLUSqThYuXYPFCfwwZ2A/a2jrw6DcA/ZS881yc5VdfYe36TViyOAB7d+9ERStrBC5bCVs7e6GjyZWenh6CN27BQn8/fNuzGypaWWFR0DKl3e+p032w4Ec/DB8yCHr6ehg9djzatmsvdKxS82fqG3T/YT+CxrTB6J6uSEjOwAD/I4iIKbrnxPH/PUKd/dew1acbtLU0cPzKQ0xZe17g1PJT3o9r9HEqLFWUmIpYCe9Fn50ndAJhlNernxUq35/wv1JeD3hilM/3u7wqr5d1NO646PMbKaGXp34QOoIg8gvL512s9bW+3CGFYbEZCnutRjUMFfZa8sSb3xERERERFVNOz2fI5MvtIhIRERERUZnASgURERERUTEsVJQcKxVERERERCQTViqIiIiIiIpjqaLEWKkgIiIiIiKZsFNBREREREQy4fAnIiIiIqJiyuu9oGTBSgUREREREcmElQoiIiIiomJ487uSY6WCiIiIiIhkwkoFEREREVExLFSUHCsVREREREQkE1YqiIiIiIiKY6mixFipICIiIiIimbBSQURERERUDO9TUXKsVBARERERkUxYqSAiIiIiKob3qSg5ViqIiIiIiEgmrFQQERERERXDQkXJsVJBREREREQyURGLxWKhQ5S27DyhEwgjv6BQ6AiCUFMtn+cT8gqU7p/uv6KpXj7PheTkFQgdQRBaGuXz/S6vXGafFTqCIG7May90BEHoaX25n9+RT94o7LWcKusr7LXkiUdrIiIiIiKSCedUEBEREREVw/tUlBwrFUREREREJBN2KoiIiIiISCYc/kREREREVAxvfldyrFQQEREREZFMWKkgIiIiIiqGhYqSY6WCiIiIiIhkwkoFEREREVFxLFWUGCsVREREREQkE1YqiIiIiIiK4c3vSo6VCiIiIiIikgkrFURERERExfA+FSXHSgUREREREcmElQoiIiIiomJYqCg5ViqIiIiIiEgmrFQQERERERXHUkWJsVJBREREREQyYaWCiIiIiKgY3qei5FipICIiIiIimbBTUQpyc3PxTc+uuP5HmFR7YmICGrnWEyiV/E0Y5wW/2T6Sx5d//w39+/ZC88au8OjTAxd/uyBgutL1IjkZUyd7o2XTRmjfpgWWBAZAJBJJbZOYmIDGbk4CJZSPXy+cQ8P6tlLLD1MnYNSwgR+0N6xvC/+5M4WOLBfPnz3D+DFeaNrIBZ3au2Pnjq1CRyp1J44dRmNnuw+WJi72AIAH9+9iqOd3aNnEBUMG9MX9u3cETlz6cnNz0adnN9wodiyPiozAoAEe+LqBC3p27YhDBw8ImFA+Prbf/7tyCX1790BjVyf07d0Dly/9LmDC/0ZDTQXHJn6NhtWNJW3WxjrYMswNN39sgxOTmqJpLVOp53zXqBLOTWuOG35tsHGIKyqZ6EjWVdBUw4+97fG/Wa3xm09LDG9ZTWH7UhqeJCZg7KhhaNbIBZ3bt8b2nzZ/sM2bN2/QsW0LHDt6SICEXxYVFcUtyoLDn2QkEongM30KYmMeSbU/f/YM3mO9PvjiqSzOnDqJK5d+R9fuPQEAjx4+wLTJ3pgwaRqaNm+Bq/+7jBlTJmL77v2oXaeusGFlJBaLMXWyNwwMDLFl205kZGTAb85MqKmpYdKU6QCA58+fYcLYUUr3fsfHxqJ5y9bwmT1P0qalqYVCcSHy8vIkbXduR8F3+iR807efEDHlbvrUiahY0Qq79x9CXGwMfGZMhVVFa7i3bSd0tFLTtn0nNPm6meRxfn4+xo4cgmYtWiE7OwuTx49Ch05dMXveQhw+uA+TvUch9PgZ6OhUEDB16RGJRPCdPlXqWJ6amoJxo0fi274e+HHhIty7cwd+s31hbm6O5i1bCRe2FH1svxMTEzBlwniM9Z6IVq3b4NcL5zHZeyyOnDgFK+tKAqb99zTVVbHEox5qf6Uv1b52oDMePn+DPquvoa29BVZ71keXpVfwLCMHzWqZYlqnOpi6NwqPU99icsfaWOPpjJ4r/wcA8O9tD3trA4zdcQuqKkDgd/WQX1CIrZcThNjFEiksLMSEsV6wc3DE7v2HkJiYAN8ZU2BuYYFOXbpJtlu9YglSXrwQMCmVZaxUyCA2Ngae/fsi6UmiVPuFX86j33e9oamhKVAy+crISMeq5UtgZ+8oaTv98wk0aNgYHgM8UbmKDfp6DIBbg4Y4d/a0gElLx+P4eNyOisQ8/4WoUbMWXFzdMHrseJz6+QQA4NdfzqP/d99AQ1P53u/H8XGoUaMWzMzMJYu+gQEMDY0kj42NTbBu9XJ4Dh4GO3sHoSOXutcZGYiKjMAIr9GwsamK1u5t0bRpc4SFXRU6WqnS1taGqZm5ZDl98jgAMcZ4T8b5M6egpaWN8ZOmoVr1Gpg0zQcVKujil3NnhI5dKmJjYzCw/3d48t6x/NdffoGZqRnGT5wMG5uq6Ni5C7p27yH5t1/WfWq/Xzx/jt59+uL7gYNRqXJleA4aAh2dCoi+fVugpCVTw0IX+8Y0QhVT6Q5voxomqGyig7mH7iIu5S02/BaPiIQMfNPAGgDQoq45rjxKxW/3U/A4NQtrzsWgbkV9GFXQgFEFDXSpXxFzD9/FrYR0hD9Ox5JTDzG0RVUB9rDk0tJSUbuuLXxmzUUVm6po1rwlGjZqgohbNyXb3LoZjj/CrsHUzFzApFSWsVMhg/Drf6BBw0bYtmufVPul33/D2HETMO0H5RwKsmJpEDp37Y7qNWpI2rp274lxEyZ/sG3mmzeKjCYXZmZmWBuyEaZmZlLtmW8yAQCXLl3EmLHemP6DrxDx5Co+LgZVbKr+4zYnjh3G69cZGDhkuGJCKZiWtja0dXRw9Mgh5OXl4XF8HCJu3UTdurZCR5ObjIx07Ni6GWPGT4ampiaib0fBqb4LVP6q06uoqKBefRdER0UIG7SUhF+//texfK9Ue9NmzeA3f+EH2yvDcQ349H67NWyEaX8dz/Ly8nA49CBy83Lh4Oj4sR/zxWlQzQRhcS/hse6aVHv9yoa4+/Q1svMKJG03E16hfhUjAEB6Vh7cqhmjmrku1FRV0MPFCkkvs/A6Ow+VTYo6KJFP0iXPffDsDSwMtGFtrC33fZKVubkFFgUth66uHsRiMSJu3cTN8OtwbdAQQNEQuPnzZmOG72xoamoInPbLoKLARVkINvzp/PnzuHbtGuzs7NC7d2+cOHECwcHBePr0KSpVqoSBAwfi22+/FSrev9LXo/9H2+fOmw8AH8yxUAbXw67h1s0b2HvwKBYt+HtITLXqNaS2i415hOt/XMM3336n6IilTt/AAF83bS55XFhYiH17dqFho8YAgDl+/gCAG9eV6/0Wi8VIePwY165exk+b16OwsBBt2nWA15jx0PirCicWi7H9p03wGDAQFSroCpxYPrS0tOAzcw4WLfDH7p3bUVBQgO49e6PXN1/28UkWhw7shZm5OdzbdQBQNAyoevWaUtuYmJoi7r1hn2VVX4+PD9uzsq4kNdznZVoazpz6GV5jxikqmlx9ar/fSUxMQO9unVFQUADvSVPKzNCnvWFPPtpubqCFF6+lh6imvsmFpWFRp2Dn/xLQpKYJTk1phvyCQmTnFWBAyB8oFANpmUXPszTQRkJaFgCgolHR84wraOLPVzny2p1S17VjGzx/9hTNW7RCm7btAQBbNoagTl1bqSGQRCUlSKdi27ZtWLFiBZo3b47Tp0/jxo0bOHPmDEaMGAFbW1vExcVh6dKlyMnJgaenpxAR6SNEIhEWzp+LGT6zoa396TMz6a9eYfqUCXCq74yWrdsoMKFirFgWhPv37mLnHuWbsFnc82dPkZOTDQ0NTQQELsefT5OwdPFCiHJEmDKj6Cxm+I0/8OJFMnr2Vt4v2AAQHxeLlq1aw3PQEMTEPMLihf5o1LgJunTtLnS0UicWi3HscCi+HzRM0ibKyf7g7KWmhiZyc3MVHU8wOTk5mDrJG6ZmZkpxsuTfMDY2wc69BxAVGYGlgYtQuUoVtP2ro1kWaWuoIS+/UKott6AQmmpFgzYs9LWgpa6GqXuikJCWhdHu1RH0XT18u/YanqbnICIhHb7d6mL6vtvQUFPBuLZFJ9M01MrWoI+gZSuRmpqKRfPnYWlQAPp864HQA/uwN/So0NG+LMpUQlAQQToV27dvx5IlS9CmTRvExcWhc+fOWLRoEXr27AkAaNmyJWxsbLB48WJ2Kr4gG0PWwtbOAU2afvpMRlpaKsZ6DYO4sBCLl6yEqmrZOth+zsplS7B753YsClqGmrVqCx1HripaWePcxaswMDCEiooKate1hbhQjLkzp2Pi1BlQU1PDhXNn8HXT5jA0NBI6rtyEXbuKw6EHceaXi9DW1oa9gyNeJCdj0/pgpexU3LsbjRcvktGuYydJm6amFnJz86S2y83Lhba2zvtPV0pZWW8xafxYJDx+jC07dkFHp3zst76+Pura2qGurR3iYmOwd9fOMt2pEOUXQqfCe51jNVXk/DUcyq+XPc5GJ+NE5DMAwNS9UfjVpyXa2FngVNRzTN9/GysHOOHq7NZ4I8rHstMP4WxjjExRvsL3RRbv5kPmikSY5TMNd6NvY9TY8TA1NfvMM4n+mSCdivT0dNSqVQsAUKVKFaipqaF2bekvaNWrV8fLly+FiEefcPb0z0hLS0Xzxq4Air5UAMAv587i0rVwvEhOxqgRgwEA6zdvh7GJiVBR5WLRQn8c3L8X8wMCy/QHa0m831moWq06RCIRXmdkwNjEBFf/dxkjRo0VJpyC3L0bjSo2NlLVubq2dti8MUTAVPJz7cplOLu4wsDAUNJmbmGJtLRUqe1epqbCzFz5v4RkZmZi3KgReJKYiA1btsLmM3OMlEFszCNkZGTAxdVN0la9Rk3cuH5dwFSye5GRg1qWelJt5vqaSHlTNLTJ3toAIb/GSdZl5RYgITULVn8Nc0pMy0KvVVdhoquJNzl5qGJaAQWFYjxNz1bcTvxHaWmpiIqMQGv3tpK26jVqIi8vD7ejIhHz6BGWLwkEAOTkZCPA3w/nTp/C6uCNQkUWHG9+V3KCnEZu0KABVq5ciZiYGCxduhSamprYvHmzpJSen5+PkJAQ1KunvPd4KIvWb96GvQePYvf+Q9i9/xBatGyNFi1bY/f+Q8jOysL4MSOgqqqKDVu2w9zCQui4pWp98BqEHtiHgMCl6Nipi9BxFOLq/y6jbcvGyMn++wPz4YP7MDQygrGJCdJfvcKfSU/gVN9FwJTyZ2FugSeJCcjL+3uoz+P4uDIzvryk7kRHoZ6T9Hvq4FgPtyNvQSwWAygaIhUVeRMOjsp1X5b3FRYWYsrE8fgzKQmbtu5AjZq1hI6kEBd/+xX+frMl7zcA3L1zB9WqVxcwlewinmTAzsoAWup/f/VxqWqMyMR0AMCL1yLUtPh7bpiGmgoqGesg6VU2VFSAzUNdUdtSDy/f5iKvQIyWdc1x98/XeCsqeP+lvjhPk5IwbdJ4vEhOlrTdu3sHBgaGOHLiDPYcOCxZzM0tMGqMN2b7zRcwMZVFgnQq/Pz8kJSUhK5du2Lv3r2YM2cOKlasiBYtWsDDwwPNmzfHlStX4OurfFfTKcsqWlmjchUbyaKrqwtdXV1UrmKDLZs3ICnpCfz8AwAUTexMTU1RiqukxMXFYuP6YAweOgLOLq6SfUtNTRE6mlzVc3KGtpY25s+bjYTH8fjf5d+xenkQPP8aax8b8whaWlpK++X6nRat3KGuroF5c2Yh4XE8Lv52AZs3hqD/AOUcmhkb8+iDCy+4t+2AzDdvsDwoAPGxMVgeFIDs7Gy0ad9RoJSKceTQQdz4Iwxz5vlD30Bf8u8+IyNd6Ghy1aVrd6SmpGDV8qVISHiMfXt24ecTxzB0+Eiho8nketxLPMvIwcJvHVDTQhcjWlZDvcqGOHj9TwDAgetJGNW6OlrVNUc1swrw722Pt6J8/HovBWIxkJ1XgMmdasPGtALa2FlgbJsaWP9b3Gde9ctg5+AIWzt7zJvri7jYGFy+dBErlwVhxKixUp/rlavYQE1dDcamJrCwtBQ6tqB487uSE2T401dffYV9+/bh9evX0NbWhuZf1/dv2rQp7ty5AwsLC7i7u0NPT+8zP4m+FBfOn4UoJweDv5eewNi1e09JR6Os+u3CLygoKMCmDcHYtCFYat2t2/cFSiV/urq6WLluI5YHBWBQ/z6ooKuLXt98B8/BRZ2Kly9ToaevL7nMqLLS19fH+s1bERiwAAM8+sDY2ATDvUYr7WTdVy/ToG9gINWmq6eHJavWYfGCeTh66ABq1KqNZatDlObGd5/yy7mzKCwshPfYUVLtrm4NsGnrDoFSyZ/lV19h7fpNWLI4AHt370RFK2sELlsJWzt7oaPJpFAMjN1+Cwu+sUfo+CZISMvCuB238Cyj6MpNW36PhwqAmd3qwkhXA7cS0jFk8w3k/jW52+/wXfzY2x6HvJsgLTMXC47dw/k7ZeNGcWpqali2ci0WB8zHYE8P6OjowKP/9+inpCdHlFlycjIWLFiAa9euQUtLC507d8bkyZOhpaWFJ0+eYPbs2YiIiICVlRV8fX3RrNnf82D/97//YeHChXjy5AmcnJywYMECVK5cudSyqYiL1zeVRHbe57dRRvkFhZ/fSAmpqSr3l9pPyStQun+6/4qmunJN/v+3cvK+/CEW8qClUT7f7/LKZfZZoSMI4sa89kJHEISe1pf7+R37QnFzZWpY/LuLP4jFYnh4eMDAwADTp09HRkYGfH190aZNG0yfPh09evRA7dq1MXr0aJw/fx7BwcH4+eefYWVlhadPn6JLly4YP348mjdvjrVr1yI2NhbHjh0rtZODgt2ngoiIiIiI/p24uDhERETgypUrMPvrhrze3t5YvHgxWrRogSdPnmDv3r2oUKECatSogatXryI0NBTjx4/HgQMH4ODggKFDhwIAAgIC0LRpU/zxxx9o1KhRqeTjKSAiIiIiouK+wFtqm5ubY9OmTZIOxTuZmZmIjIyEnZ0dKlT4e1iqq6srIiIiAACRkZFwc/v7im46Ojqwt7eXrC8NrFQQEREREQkkNzf3g5uJampqSuYcv2NgYIDmzZtLHhcWFmLnzp1o3LgxUlJSYPHelTdNTU3x/PlzAPjs+tLASgURERERUTEqCvxv/fr1cHV1lVrWr1//2YxBQUG4e/cuJk2ahOzs7A86IZqampLOyufWlwZWKoiIiIiIBOLl5YUhQ4ZItb3fAXhfUFAQtm3bhuXLl6N27drQ0tJCenq61Da5ubmSG7dqaWl90IHIzc2FwXtX+5MFOxVERERERMUo8mrpHxvq9E/8/f2xZ88eBAUFoUOHDgAAS0tLxMTESG2XmpoqGfJkaWmJ1NTUD9bb2trKmP5vHP5ERERERFQGrFmzBnv37sWyZcvQpUsXSbuTkxPu3LmDnJwcSVt4eDicnJwk68PDwyXrsrOzcffuXcn60sBOBRERERFRMV/gxZ8QGxuLdevWYcSIEXB1dUVKSopkadiwISpWrAgfHx88evQIGzZsQFRUFPr06QMA+Oabb3Dz5k1s2LABjx49go+PDypVqlRql5MF2KkgIiIiIvri/fLLLygoKEBwcDCaNWsmtaipqWHdunVISUlB7969cezYMaxduxZWVlYAgEqVKmH16tUIDQ1Fnz59kJ6ejrVr15baje8A3lFbqfCO2uUL76hdvvCO2lQe8I7a5cuXfEftx2k5n9+olFQ11VbYa8kTj9ZERERERCQTdiqIiIiIiEgmvKQsEREREVExKiWaQk0AKxVERERERCQjViqIiIiIiIpR5M3vlAUrFUREREREJBNWKoiIiIiIimGhouRYqSAiIiIiIpmwUkFEREREVAznVJQcKxVERERERCQTViqIiIiIiKSwVFFSKmKxWCx0iNKWnSd0AmGwVFe+FBQq3T/dfyU3v1DoCILQVC+fhWU11fJ5YHuTnS90BEHoaKoJHUEQ9tNOCB1BEAmrugkd4ZOSXuUq7LUqGWsq7LXkiZUKIiIiIqJieKK25MrnqS8iIiIiIio1rFQQERERERXDQkXJsVJBREREREQyYaWCiIiIiKgYzqkoOVYqiIiIiIhIJqxUEBEREREVo8JZFSXGSgUREREREcmEnQoiIiIiIpIJhz8RERERERXH0U8lxkoFERERERHJhJUKIiIiIqJiWKgoOVYqiIiIiIhIJqxUEBEREREVw5vflRwrFUREREREJBNWKoiIiIiIiuHN70qOlQoiIiIiIpIJKxVERERERMWxUFFirFQQEREREZFM2KkoJc+fPcP4MV5o2sgFndq7Y+eOrUJHUojEhASMGjEMjd2c0aFNK2zdsknoSAo3bvRIzPb9QegYcvEiORnTJnujVdNG6NCmBZYGBkAkEklt8+bNG3Ro0wLHjhwSKGXpOnHsMBo7232wNHGxBwBcuXQRnt/1QuuvXTGgb0/8/tsFgROXnn96v589e4rxo0fi6wb10b1ze5w9fUrgtPIjEokwd7YvmjV2Q5uWzbBt6xahI5Wq3NxcePbtgZs3/pC0Pf0zCRPGDEPbZm74/ttu+OPalY8+9+ypExg3crCCksrHk8QEjB01DM0auaBz+9bY/tNmybrbkREY4umBZo1c0LtbRxwOPSBg0v9OU10VZ39oicY1TaXabcwq4MGSzh9sf2pGCySs6ia11K6oL1k/o1td3FzYHpEBHeDT3bZcXBlJRYGLsuDwp1IyfepEVKxohd37DyEuNgY+M6bCqqI13Nu2Ezqa3BQWFmLcmJGwd3DEvtDDSExIwA/TJsPCwhKdu3YTOp5CnPr5JC79fhHde/QSOkqpE4vFmDbZGwYGhti8bScyMjIwb85MqKqpYdKU6ZLtVi1fgpQXLwRMWrratu+EJl83kzzOz8/H2JFD0KxFKzx6+AA/TPHGuIlT8XWzFgi7egW+0ybip537UatOXQFTy+6f3u/xEyZjwhgvWFeqjN37D+HG9T8wy2c6qteogZq1agsdvdQtWxKIu9HR2LhlG54+fYrZvjNgVdEK7Tp0FDqazEQiEebNmo74uBhJm1gshs/U8ahRszY27diHS79dgO/UCdh58Bi++spKst3NG2EIXOCHunYOQkQvFYWFhZgw1gt2Do7Yvf8QEhMT4DtjCswtLNCgUWOMHzMSffp6YN78Rbh39w7mzfGFmbk5mrdoJXT0f01LXRWrBrmgjpWBVHtFI21s8WoEbU01qXZVFaC6uR6+XXkF8S/eStpfvs0FAIxoXR09XCth5KYb0FBTwQpPZ6RlirDhQpz8d4bKFHYqSsHrjAxERUZgjp8/bGyqwsamKpo2bY6wsKtK3alIS0tFnbq2mDXHD7q6erCxqYqGjZvg1s3wctGpyEhPx/KlgbB3cBQ6ilw8jo/H7ahInPv1MkzNzAAAo8eOx/KlgZJOxa2b4fgj7BrMzMyFjFqqtLW1oa2tLXm8bfMGAGKM8Z6MjcFr4NqgEb7r7wkAqFzFBpcu/orz506X+U7FP73fLi5ueJ78HFt27IGenh6qVquO/12+hMiIW0rXqcjKysLh0ANYG7IRtnb2sLWzR2zMI+zds6vMdyri42Iwb9Z0iMViqfabN8LwNOkJQrbsgo5OBVStVgM3rl/DyaOHMcxrLABgy4Z12Ll1IypVthEieqlJS0tF7bq28Jk1F7q6eqhiUxUNGzVBxK2bePv2LUzNzDBuwmQAQBWbqrhxPQynfz5RZjoVtb7Sw8qBLh9UEto7foUAj3p48Trng+dUNq0ADXVVRCakQ5Rf+MH6IS2rY9nP93Ej7iUAYNGxe5jSpa7SdyrKQzWmtHH4UynQ0taGto4Ojh45hLy8PDyOj0PErZuoW9dW6GhyZW5ugaClK6CrqwexWIxbN8Nx88Z1uDVsKHQ0hVi6ZDG6duuBGjVqCh1FLszMzLAmZKPkC+Y7mW8yARQNofD3m40fZs6GhqaGEBHlLiMjHTu2bsaY8ZOhqamJzt16YKz35A+2e5uZKUC60vVP7/eN62Fo2Kgx9PT0JO3LVq3FN99+p+iYcvfwwX3k5+ejfn1nSZuziytuR0WisPDDL1xlScTNG3BxbYj1P+2War9zOwq169pBR6eCpK2ekwvu3I6QPL4e9j8sXb0BLd3L9okyc3MLLApaLvncirh1EzfDr8O1QUN83bQZ/H5c+MFzMjPfCJD0v2lU0xRXH6Wh1zLp4Wvu9hZYevIB5oXe+eA5tb7Sx9NX2R/tUFgYaMHaRAdhsS8lbdfjXqKyaQVYGGiV/g5QmSZ4paKgoABv3rxBXl4e9PT0oKOjI3SkEtPS0oLPzDlYtMAfu3duR0FBAbr37I1e33wrdDSF6dTOHc+ePUWLlq3Rtl0HoePIXdi1q7h54wYOHjmOBT/6CR1HLvQNDPB10+aSx4WFhdi3ZxcaNmoMANi8MQR169pKDRVSNocO7IWZuTnc//qbrla9htT6uNhHuPHHNfTqU/a/XP/T+/1nUhIqWltj1fKlOHniKIyMjDFqzHi0btNWwMTykZqSAiMjY2hoakraTE3NIBKJkJ6eDhMTEwHTyaZXH4+PtqelpsDMzEKqzcTUFC9eJEseB2/eCQBS8zDKuq4d2+D5s6do3qIV2rRtDzU1NVhZV5Ksf5mWhjOnf4bX6HECpiyZnZcTPtr+w94oAPhgjgUA1PxKD3kFhdgysiEcqxgi7sVbLDxyF5GJ6bAwLKravsj4u8KR8qZontVXRtp48Vr0wc9TFrxPRckJVqk4f/48PDw84OTkhCZNmqBFixZwcXHB119/jYkTJ+LOnQ9701+y+LhYtGzVGtt37cO8+QE4f/Y0Tp44JnQshVm6YhVWrQ3Bgwf3ELQ4QOg4ciUSiTB/3lz4zJojNUxG2a1cFoT79+5irPdExMXGIHT/PkyZ7iN0LLkRi8U4djgU33p8/9H16a9ewWfqRNRzckaLVu4KTid/xd/vrKwsHD96GK9fZ2DF6mB07d4D06dMwN07t4WOWeqyc7KhWaxDAUDyOC83V4hIcpeTk/NBtVFDQ1Np9/edoGUrsXx1MB4+uI+lQdKfWzk5OZg22RtmpmborQQnDf5JDQs9GFbQwN6riRgS8gcePX+D3eOaoKKRNnQ0iuZfFK9i5P71/5rqah/9eVR+CVKpOHz4MBYtWoThw4djzJgxePbsGbZu3QoPDw9UrVoVv/32GwYMGICVK1eiZcuWQkQskbBrV3E49CDO/HIR2trasHdwxIvkZGxaH4wuXbsLHU8h3s0ryBWJ4DNjKqZMnS51pk+ZhKxbAzt7BzRt1vzzGyuJlcuWYPfO7VgUtAw1atbC0IH9MWrs+A+GyiiTe3ej8eJFMtp17PTBurS0VHiPHo7CwkIsDFoBVVXlGkla/P2uWas21NTVYGRoBN/ZflBVVYWtnT1uhYcj9MB+2Nkr15wiLS0t5L73ZfrdY2U9iaCppYnX6dlSbXl5udDWLnsjB0ri3d9urkiEWT7TMGnKdGhoaCIr6y0me49FYsJjbN62q0yOoCiJH/ZGQUdTDZk5+QCAWftvw62aCXo3qIRLD1IAFE3+Fkk6E0XHu5zcAmECKwjnVJScIJ2KkJAQBAYGSnUYGjdujO+//x4XL15Ey5YtYWdnhyVLlpSJTsXdu9GoYmMj9YFT19YOmzeGCJhK/tJSUxEZGQH3YkMgqteoiby8PGS+zYSxZtkdJvBPTp86ibTUVDR2KxpznZdX9IXj3NkzuHbjlpDR5GLxQn8c3L8X8wMC0aZdBzx9+iciI27h4YMHWL4kEACQk5ONhf5+OHv6FNaEbBQ4cem4duUynF1cYWBgKNX+4kUyxo0cAgBYt3EbjMvwcJiPef/9BgAzM3OoQEWq82RTtRoePXwgVEy5sbCwRHr6K+Tn50NdvegjMjU1Bdra2tA3MPjMs8smc3NLxMfGSrW9TEtVypMGaWmpiIqMQGv3j3xuZb6FhkYuvMeMwJPERIRs2ooqNlWFC6sgBYViSYfindgXmfjKSBvP04uGPZkbaCHpZVHH00K/aC7FxyZ9U/kmyOm1ly9fwtLSUqrNwsICaWlpePXqFYCiTkZSUpIQ8UrMwtwCTxITJF8uAeBxfJzU2Exl9OefSZg8YRySk/8ed3v3bjSMTUxgbKxcX7SK27x1Bw4ePo79oUewP/QIWrZyR8tW7tgfekToaKVuffAahB7Yh4DApejQqQuAoi9dR06ewZ6DhyWLubkFRo31xpx58wVOXHruREehnpOLVFt2dhYmjR0JFVVVBG/aBnMLi088u2z62PsNAI71nBAb8wgFBX+fmYyPj4WVtbUQMeWqTl1bqKurIyoyQtJ262Y47B0cla4i9Y69Yz08fHAXopy/vyRGRdyEvaOTgKnk42lSEqZNGo8XxT637t29A2NjExgaGmLapPFISkrChp92oEbNWgImVZy945tgQse/r+KmogLUtTJAbHImXrwWIellFhpU//sz3a2GKZJeZin1fAr6bwQ5QjZp0gR+fn74888/Afw1Rn3+fFhZWcHU1BQZGRlYv349HBzKxrWwW7Ryh7q6BubNmYWEx/G4+NsFbN4Ygv4DPIWOJlf2Do6ws7PH3Fm+iI2JwaXfL2L5kiCMGDlK6GhyZWVljSo2NpJFV1cXurq6qGJTti+1+L64uFhsWh+MwUNHoL6LK1JTU5CamoL09FeoUsVGalFTV4OJiQks3jtZUJbFxjz6YGL21s0bkJT0BHP+ukJMWmoK0lJTkPmm7Fwd5lM+9X6npqagY+euKBQXImD+PCQmJmD/3t343+VLSnkxCh0dHXTr0RPzf/RD9O0oXPjlPLZv3YL+3w8UOprc1HdpAAvLr7Bw3izExcZgx9aNuHvnNrr06C10tFJn5+AIWzt7zJvri7jYGFy+dBErlwVh6IhROHLoIG5cD8NsP3/o6+tL/v4zMtKFji1X56OTMaxVdbR1sER1C13493GEgY46DoQ9AVA0+fuH7rZoXNMUjWua4odudfHTxXiBU9OXSJDhT35+fhgzZgzatm0LExMTvH79Gubm5li1ahUAYPTo0cjOzsby5cuFiFdi+vr6WL95KwIDFmCARx8YG5tguNdopbzcYnFqampYsWYdAhb4Y+CA76Cjo4P+33sq9YdveXLxwi8oKCjApg3B2LQhWGrdzdv3BUqlOK9epn0w3OW3X85BlJODYZ7SV9Hp3K2npKNRVn3u/Q7esAUL/f3Qt1c3VLSyQkDQMtja2QuUVr6mTvfBgh/9MHzIIOjp62H02PFo26690LHkRk1NDYuWrsYi/zkY7vktrCtVwcKgVVI3vlMWampqWLZyLRYHzMdgTw/o6OjAo//36DfAE+NHj0BhYSEmjpM+Mebq1gAbtuwQKLH8bfo1DlrqqpjXxwFm+lqISEjHgLXX8FZUVJlc/0sMzPQ1sWG4G/ILxdh3NRGbflXue1TQf6Mifv8uOAoUHR2NJ0+ewMzMDE5OTpIrbGRkZMDQ0PAzz/607LzSSli2cFJR+VJQKNg/XUHlfuRa6uXBu8mR5Y2aavk8sL3Jzv/8RkpIR7N8XlHIftoJoSMIImHVl3uj3PRsxU1EN9JRjr97Qe9T4eDg8NEhTrJ0KIiIiIiISLEEv/kdEREREdGXhDe/K7nyWU8nIiIiIqJSw0oFEREREVExnKdacqxUEBERERGRTFipICIiIiIqhoWKkmOlgoiIiIiIZMJKBRERERFRcSxVlBgrFUREREREJBNWKoiIiIiIiuF9KkqOlQoiIiIiIpIJKxVERERERMXwPhUlx0oFERERERHJhJUKIiIiIqJiWKgoOVYqiIiIiIhIJqxUEBEREREVx1JFibFSQUREREREMmGngoiIiIioDBCJRPD19YWbmxuaNWuGLVu2CB1JgsOfiIiIiIiK+VJvfhcYGIjo6Ghs27YNT58+xYwZM2BlZYWOHTsKHY2dCiIiIiKiL11WVhYOHDiAjRs3wt7eHvb29nj06BF27dr1RXQqOPyJiIiIiKgYFRXFLf/W/fv3kZ+fD2dnZ0mbq6srIiMjUVhYKIffQsmwUkFEREREJJDc3Fzk5uZKtWlqakJTU1OqLSUlBcbGxlLtZmZmEIlESE9Ph4mJiULyfopSdip0NIROQKQIX+Z4T3nT1VQTOgKR3GnrK+XHM31CwqpuQkeg92gr8J/g6tXrsWbNGqm2cePGYfz48VJt2dnZH3Q03j1+v1MiBB61iIiIiIgE4uXlhSFDhki1vd95AAAtLa0POg/vHmtra8sv4L/ETgURERERkUA+NtTpYywtLfHq1Svk5+dDXb3oK3xKSgq0tbVhYGAg75ifxYnaRERERERfOFtbW6irqyMiIkLSFh4eDkdHR6iqCv+VXvgERERERET0j3R0dNCzZ0/4+fkhKioK58+fx5YtWzBw4EChowEAVMRisVjoEERERERE9M+ys7Ph5+eHs2fPQk9PD8OGDcPgwYOFjgWAnQoiIiIiIpIRhz8REREREZFM2KkgIiIiIiKZsFNBREREREQyYaeilIhEIvj6+sLNzQ3NmjXDli1bhI6kULm5uejatSvCwsKEjqIQycnJ8Pb2RsOGDdG8eXMEBARAJBIJHUvuEhISMGzYMDg7O6NVq1bYtGmT0JEUbuTIkfjhhx+EjqEQ586dQ506daQWb29voWPJXW5uLubNm4cGDRrg66+/xrJly6Ds0w8PHTr0wXtdp04d1K1bV+hocvfs2TN4eXnBxcUF7u7u2Lp1q9CRFCItLQ3e3t5wc3NDu3btcOjQIaEjURnHm9+VksDAQERHR2Pbtm14+vQpZsyYASsrK3Ts2FHoaHInEokwZcoUPHr0SOgoCiEWi+Ht7Q0DAwPs2rULGRkZ8PX1haqqKmbMmCF0PLkpLCzEyJEj4ejoiMOHDyMhIQGTJ0+GpaUlunXrJnQ8hTh58iQuXryIXr16CR1FIWJiYtC6dWv4+/tL2rS0tARMpBjz589HWFgYNm/ejLdv32LSpEmwsrKCh4eH0NHkpnPnzmjevLnkcX5+PgYNGoRWrVoJF0pBJk6cCCsrKxw6dAgxMTGYOnUqrK2t0a5dO6GjyY1YLMbYsWNRWFiI7du3Izk5GTNmzICenh7at28vdDwqo9ipKAVZWVk4cOAANm7cCHt7e9jb2+PRo0fYtWuX0ncqYmJiMGXKFKU/i1dcXFwcIiIicOXKFZiZmQEAvL29sXjxYqXuVKSmpsLW1hZ+fn7Q09ND1apV0aRJE4SHh5eLTkV6ejoCAwPh6OgodBSFiY2NRe3atWFubi50FIVJT09HaGgofvrpJ9SrVw8AMHToUERGRip1p0JbWxva2tqSx+vXr4dYLMbUqVMFTCV/GRkZiIiIgL+/P6pWrYqqVauiefPmuHr1qlJ3KqKjo3Hr1i2cP38elStXhp2dHYYPH47NmzezU0H/GYc/lYL79+8jPz8fzs7OkjZXV1dERkaisLBQwGTy98cff6BRo0bYt2+f0FEUxtzcHJs2bZJ0KN7JzMwUKJFiWFhYYMWKFdDT04NYLEZ4eDiuX7+Ohg0bCh1NIRYvXowePXqgZs2aQkdRmNjYWFStWlXoGAoVHh4OPT09qb/rkSNHIiAgQMBUipWeno6NGzdiypQp0NTUFDqOXGlra0NHRweHDh1CXl4e4uLicPPmTdja2godTa6ePHkCExMTVK5cWdJWp04dREdHIy8vT8BkVJaxU1EKUlJSYGxsLHXwNTMzg0gkQnp6unDBFKB///7w9fWFjo6O0FEUxsDAQGqYQGFhIXbu3InGjRsLmEqx3N3d0b9/fzg7O6NDhw5Cx5G7q1ev4saNGxgzZozQURRGLBYjPj4ely9fRocOHdC2bVssWbIEubm5QkeTqydPnsDa2hpHjhxBx44d0aZNG6xdu1bpTxAVt2fPHlhYWCh9pR0oGs43Z84c7Nu3D05OTujUqRNatGiBb7/9VuhocmVmZoY3b94gOztb0vb8+XPk5+fjzZs3AiajsoydilKQnZ39wdmcd4+V/QOYgKCgINy9exeTJk0SOorCrFq1CiEhIbh3757Sn8EViUSYO3cu5syZIzU8RNk9ffpUcmxbsWIFZsyYgePHjyMwMFDoaHKVlZWFhIQE7N27FwEBAZgxYwZ27NhRbibvisViHDhwAN9//73QURQmNjYWrVu3xr59+xAQEIDTp0/j2LFjQseSKycnJ1hYWMDf31/yN//TTz8BACsV9J9xTkUp0NLS+qDz8O5xefoSUh4FBQVh27ZtWL58OWrXri10HIV5N69AJBJh6tSpmD59utIOk1izZg0cHBykqlPlgbW1NcLCwmBoaAgVFRXY2tqisLAQ06ZNg4+PD9TU1ISOKBfq6urIzMzE0qVLYW1tDaCog7Vnzx4MHTpU4HTyd/v2bSQnJ6NLly5CR1GIq1ev4uDBg7h48SK0tbXh6OiI5ORkBAcHo3v37kLHkxstLS2sWLECEydOhKurK0xNTTF8+HAEBARAT09P6HhURrFTUQosLS3x6tUr5OfnQ1296FeakpICbW1tGBgYCJyO5MXf3x979uxBUFBQuRgClJqaioiICLRt21bSVrNmTeTl5SEzMxMmJiYCppOfkydPIjU1VTJn6t0JgzNnzuDWrVtCRpM7IyMjqcc1atSASCRCRkaG0r7f5ubm0NLSknQoAKBatWp49uyZgKkU59KlS3Bzc4OhoaHQURQiOjoaNjY2UicA7ezsEBISImAqxahXrx4uXLggGcJ95coVGBsbQ1dXV+hoVEZx+FMpsLW1hbq6OiIiIiRt4eHhcHR0hKoqf8XKaM2aNdi7dy+WLVtWbs7oJSUlYdy4cUhOTpa0RUdHw8TERGm/YALAjh07cPz4cRw5cgRHjhyBu7s73N3dceTIEaGjydWlS5fQqFEjqTHX9+7dg5GRkVK/305OThCJRIiPj5e0xcXFSXUylFlUVBRcXFyEjqEwFhYWSEhIkBptEBcXh0qVKgmYSv7S09PRr18/vHr1Cubm5lBXV8dvv/1Wbi68QfLBb7ylQEdHBz179oSfnx+ioqJw/vx5bNmyBQMHDhQ6GslBbGws1q1bhxEjRsDV1RUpKSmSRZk5OjrC3t4evr6+iImJwcWLFxEUFIRRo0YJHU2urK2tYWNjI1l0dXWhq6sLGxsboaPJlbOzM7S0tDBr1izExcXh4sWLCAwMxPDhw4WOJlfVq1dHq1at4OPjg/v37+PSpUvYsGED+vXrJ3Q0hXj06FG5usKZu7s7NDQ0MGvWLMTHx+PChQsICQmBp6en0NHkysjICFlZWQgKCsKTJ09w4MABhIaGKv2/b5IvFXF5usGAHGVnZ8PPzw9nz56Fnp4ehg0bhsGDBwsdS6Hq1KmD7du3o1GjRkJHkasNGzZg6dKlH1334MEDBadRrOTkZPj7++Pq1avQ0dHB999/Dy8vL6ioqAgdTWHe3U170aJFAieRv0ePHmHhwoWIiIiArq4uPDw8MHbsWKV/v9+8eQN/f3+cO3cOOjo66N+/f7nYb6BoSMzatWvL1RyimJgYLFiwAFFRUTAxMcGAAQMwaNAgpX+/4+LiMHfuXNy+fRuVKlXClClT0Lp1a6FjURnGTgUREREREcmEw5+IiIiIiEgm7FQQEREREZFM2KkgIiIiIiKZsFNBREREREQyYaeCiIiIiIhkwk4FERERERHJhJ0KIiIiIiKSCTsVREREREQkE3YqiKjccHd3R506dSSLvb09OnbsiK1bt5bq63h6emL16tUAiu7A/e4u3P8kNzcX+/fv/8+veejQIbi7u390XVhYGOrUqfOff3adOnUQFhb2n567evVqeHp6/ufXJiKiskFd6ABERIrk6+uLzp07AwDy8/Nx7do1zJw5E0ZGRujZs2epv97MmTP/1XYnT55ESEgI+vbtW+oZiIiI5I2VCiIqV/T19WFubg5zc3NUrFgRvXr1QpMmTXD27Fm5vZ6+vv5ntxOLxXJ5fSIiIkVgp4KIyj11dXVoaGgAKBq65O/vjzZt2qBVq1bIzMzEs2fPMGrUKDg5OcHd3R1r1qxBQUGB5Pnnzp1Dhw4dUL9+ffz4449S694f/nT06FF07NgRTk5O8PDwwN27dxEWFgYfHx/8+eefqFOnDpKSkiAWi7F27Vo0a9YMbm5uGDVqFJ4+fSr5OcnJyRg+fDjq16+PXr16ITEx8T/vf2ZmJnx8fNCkSRM4ODigY8eOOH/+vNQ2169fR/v27eHk5IQJEyYgIyNDsu7hw4fw9PREvXr10KFDB+zates/ZyEiorKJnQoiKrfy8vJw9uxZXLlyBW3atJG0Hzp0CEFBQVizZg10dXUxbtw4mJqa4vDhwwgICMDx48cREhICAIiJicHEiRPRr18/hIaGIj8/H+Hh4R99vUuXLmHmzJkYNGgQjh07BgcHB3h5ecHZ2Rm+vr746quvcPnyZVSsWBE7d+7E8ePHsXTpUuzbtw+mpqYYOnQo8vLyAAATJkxAYWEhDhw4gBEjRmDbtm3/+fewYMECxMfHY8uWLThx4gTc3Nwwc+ZM5ObmSrbZtWsXZs6ciV27diE+Ph4BAQEAgJycHIwYMQKurq44duwYZsyYgXXr1uHIkSP/OQ8REZU9nFNBROXK3Llz4e/vD6DoC7G2tjYGDRqE7t27S7Zp1aoVXFxcAABXr17F06dPceDAAaiqqqJ69eqYMWMGfHx8MHbsWISGhsLNzQ2DBw8GAMyePRu//vrrR19737596Nq1K/r16wcAmD59OjQ0NJCRkQF9fX2oqanB3NwcALBp0ybMnTsXjRo1AgD8+OOPaNasGS5duoTKlSvj1q1b+PXXX2FlZYVatWohOjoap0+f/k+/kwYNGmDIkCGoXbs2AGDo0KE4cOAA0tLSULFiRQDAuHHj0LJlSwDArFmzMGTIEMyaNQunTp2CqakpJk6cCACoWrUq/vzzT2zfvl0uc1SIiOjLxE4FEZUr3t7eaN++PQBAS0sL5ubmUFNTk9rG2tpa8v+xsbFIT0+Hq6urpK2wsBA5OTl49eoVYmNjYWtrK1mnoaEh9bi4+Ph4eHh4SB5rampixowZH2z39u1bPH/+HJMmTYKq6t8F5ZycHDx+/BgikQhGRkawsrKSrHN0dPzPnYqePXvi/Pnz2L9/P+Li4nDnzh0AkBrG5ejoKPl/Ozs75OfnIzExEXFxcbh//z6cnZ0l6wsKCj74nRIRkXJjp4KIyhVTU1PY2Nj84zZaWlqS/8/Pz0f16tWxbt26D7Z7NwH7/UnW7+ZnvE9d/d8dct99mV+5ciWqVasmtc7Q0BBXr17916/5b0yfPh23bt1Cjx490K9fP5ibm+O7776T2qZ4J+Hda2toaCA/Px9NmjTBnDlz/vPrExFR2cc5FURE/6BatWp4+vQpTExMYGNjAxsbGyQlJWHVqlVQUVFBrVq1cPv2bcn2hYWFuH///kd/lo2NjdS6goICuLu7Izw8HCoqKpJ2AwMDmJqaIiUlRfKaFStWRFBQEOLj41G7dm1kZGQgISFB8px79+79p/3LzMzEiRMnsHz5cnh7e6Ndu3aSSdjFOy4PHz6U/H9UVBQ0NDRQqVIlVKtWDfHx8ahUqZIka0REBHbs2PGf8hARUdnETgUR0T9o1qwZrK2tMW3aNDx48AA3btzA7NmzoaOjAzU1NfTt2xfR0dEIDg5GXFwcFi9eLHWVpuI8PT1x7NgxHD58GAkJCQgICIBYLIa9vT10dHSQkZGBx48fIz8/H4MHD8aKFStw4cIFPH78GLNmzcLNmzdRvXp11KhRA02aNIGvry/u37+P8+fPY+fOnZ/dl99//11qCQsLg6amJnR0dHD27FkkJSXh0qVL+PHHHwFAaqL28uXLcfXqVURERGD+/Pnw8PCAjo4OunfvjpycHMyZMwexsbG4ePEiFixYAFNT09J5A4iIqEzg8Ccion+gpqaG4OBg+Pv7o2/fvqhQoQI6duwomQthY2OD4OBgBAQEIDg4GG3btpVMaH5fgwYNMHfuXKxduxYpKSlwcHBASEgItLW10bhxY9jY2KBbt27YvXs3hg0bhrdv32LOnDnIzMyEg4MDNm/eDENDQwBFX/Jnz54NDw8PWFlZwdPTE4cOHfrHfRkxYoTUY0tLS/z+++8ICgrC4sWLsWPHDlSqVAmjR4/GihUrcO/ePdSoUQMAMGTIEMycOROvXr1Cp06dMHXqVACAnp4eNm7ciIULF6Jnz54wMjLCgAED4OXlJdPvnYiIyhYVMe+4REREREREMuDwJyIiIiIikgk7FUREREREJBN2KoiIiIiISCbsVBARERERkUzYqSAiIiIiIpmwU0FERERERDJhp4KIiIiIiGTCTgUREREREcmEnQoiIiIiIpIJOxVERERERCQTdiqIiIiIiEgm/wdQUWC6t5KApQAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:12:56.884147Z",
     "start_time": "2025-06-12T13:12:56.732049Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#用热度图展示各指标\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "metrics_df = pd.DataFrame({\n",
    "    'Model': ['Perceptron'],\n",
    "    'Accuracy': [0.8811],\n",
    "    'Precision': [0.8810],\n",
    "    'Recall': [0.8811],\n",
    "    'F1': [0.8808]\n",
    "}).set_index('Model')\n",
    "\n",
    "# 设置可视化参数\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = sns.heatmap(metrics_df, \n",
    "                annot=True, \n",
    "                fmt=\".2%\",  # 显示百分比格式\n",
    "                cmap=\"YlGnBu\", \n",
    "                linewidths=.5,\n",
    "                cbar_kws={'label': 'Score Scale'},\n",
    "                annot_kws={\"size\": 12})\n",
    "\n",
    "# 增强可视化效果\n",
    "plt.title(\"Model Performance Metrics Comparison\", fontsize=14, pad=20)\n",
    "plt.xticks(rotation=45, ha='right', fontsize=12)\n",
    "plt.yticks(rotation=0, fontsize=12)\n",
    "ax.xaxis.set_label_position('top') \n",
    "ax.xaxis.tick_top()\n",
    "\n",
    "# 添加颜色条说明\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.set_label('Score (0-1 scale)', rotation=270, labelpad=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "7f15ed06640d76f3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
